Inequalities in access to prenatal care during the COVID-19 pandemic: Analysis of a population-based cohort.

BACKGROUND: Before the COVID-19 pandemic, access to prenatal care was lower among some socio-demographic groups. This pandemic caused disruptions to routine preventative care, which could have increased inequalities. OBJECTIVES: To investigate if the COVID-19 pandemic increased inequalities in access to prenatal care among those who are younger, live in rural areas, have a lower socio-economic situation (SES) and are recent immigrants. METHODS: We used linked administrative datasets from ICES to identify a population-based cohort of 455,245 deliveries in Ontario from January 2018 to December 2021. Our outcomes were first-trimester prenatal visits, first-trimester ultrasound and adequacy of prenatal care. We used joinpoint analysis to examine outcome time trends and identify trend change points. We stratified analyses by age, rural residence, SES and recent immigration, and examined risk differences (RD) with 95% confidence intervals (CI) between groups at the beginning and end of the study period. RESULTS: For all outcomes, we noted disruptions to care beginning in March or April 2020 and returning to previous trends by November 2020. Inequalities were stable across groups, except recent immigrants. In July 2017, 65.0% and 69.8% of recent immigrants and non-immigrants, respectively, received ultrasounds in the first trimester (RD -4.8%, 95% CI -8.0, -1.5). By October 2020, this had increased to 75.4%, with no difference with non-immigrants (RD 0.4%, 95% CI -2.4, 3.2). Adequacy of prenatal care showed more intensive care as of November 2020, reflecting a higher number of visits. CONCLUSIONS: We found no evidence that inequalities between socio-economic groups that existed prior to the pandemic worsened after March 2020. The pandemic may be associated with increased access to care for recent immigrants. The introduction of virtual visits may have resulted in a higher number of prenatal care visits.

Postdischarge opioid use after lumbar spine surgery among older adults in Ontario: a population-based cohort study.

BACKGROUND: Prescription opioid use places a considerable economic burden on health care systems. Older patients undergoing surgical procedures for painful conditions commonly receive opioids pre- and postoperatively, and are susceptible to adverse reactions. This study explores predictors of prolonged postoperative opioid use among older patients after lumbar spine surgery and the consequences in terms of health care utilization and costs. METHODS: We conducted a retrospective population-based cohort study using Ontario administrative data from older adults undergoing spine surgery between 2006 and 2017. Data were analyzed from 90 days preoperatively to 1 year after hospital discharge, with last postoperative opioid prescriptions stratified into 90-day increments. We used multivariable ordinal logistic regression to identify predictors of long-term opioid use and generalized linear modelling to examine resource utilization and health care costs (2021 Canadian dollars). RESULTS: Of 15 109 patients included, 40.8% received preoperative opioid prescriptions. Preoperative opioid use strongly predicted prolonged postoperative use (odds ratio [OR] 4.47, 95% confidence interval [CI] 4.16-4.79), with 48.3% of patients who received preoperative opioids continuing to use opioids for longer than 9 months, relative to 12.7% of those without preoperative use. Several other risk factors for prolonged use were identified. Patients receiving long-term postoperative opioids incurred greater health care costs relative to those with opioids prescribed for fewer than 90 days (OR 1.49, 95% CI 1.44-1.54). CONCLUSION: Among older adults undergoing spine surgery, preoperative opioid use was a strong predictor of prolonged postoperative use, which was associated with increased health care costs. These results form an important baseline for future studies evaluating strategies to reduce opioid use targeting older surgical populations.

Genomic hallmarks of localized, non-indolent prostate cancer.

Prostate tumours are highly variable in their response to therapies, but clinically available prognostic factors can explain only a fraction of this heterogeneity. Here we analysed 200 whole-genome sequences and 277 additional whole-exome sequences from localized, non-indolent prostate tumours with similar clinical risk profiles, and carried out RNA and methylation analyses in a subset. These tumours had a paucity of clinically actionable single nucleotide variants, unlike those seen in metastatic disease. Rather, a significant proportion of tumours harboured recurrent non-coding aberrations, large-scale genomic rearrangements, and alterations in which an inversion repressed transcription within its boundaries. Local hypermutation events were frequent, and correlated with specific genomic profiles. Numerous molecular aberrations were prognostic for disease recurrence, including several DNA methylation events, and a signature comprised of these aberrations outperformed well-described prognostic biomarkers. We suggest that intensified treatment of genomically aggressive localized prostate cancer may improve cure rates.

Amphetamine-Related Emergency Department Visits in Ontario, Canada, 2003-2020.

OBJECTIVES: Despite unregulated amphetamine use increasing, there are limited data on related emergency department (ED) visits in Canada. Our primary objective was to examine trends in amphetamine-related ED visits over time in Ontario, including by age and sex. Secondary objectives were to examine whether patient characteristics were associated with ED revisit within 6 months. METHODS: Using administrative claims and census data, we calculated annual patient- and encounter-based rates of amphetamine-related ED visits from 2003 to 2020 among individuals 18+ years of age. We also performed a retrospective cohort study of individuals with amphetamine-related ED visits between 2019 and 2020 to determine whether select factors were associated with ED revisit within 6 months. Multivariable logistic regression modelling was used to measure associations. RESULTS: The population-based rate of amphetamine-related ED visits increased nearly 15-fold between 2003 (1.9/100,000 Ontarians) and 2020 (27.9/100,000 Ontarians). Seventy-five percent of individuals returned to the ED for any reason within 6 months. Psychosis and use of other substances were both independently associated with ED revisit for any reason within 6 months (psychosis: AOR = 1.54, 95% CI = 1.30-1.83; other substances: AOR = 1.84, 95% CI = 1.57-2.15), whereas having a primary care physician was negatively associated with ED revisit (AOR = 0.77, 95% CI = 0.60-0.98). CONCLUSIONS: Increasing rates of amphetamine-related ED visits in Ontario are cause for concern. Diagnoses of psychosis and the use of other substances may serve to identify individuals who are most likely to benefit from both primary and substance-specific care.

Length of initial prescription at hospital discharge and long-term medication adherence for elderly, post-myocardial infarction patients: a population-based interrupted time series study.

BACKGROUND: Preliminary evidence suggests that providing longer duration prescriptions at discharge may improve long-term adherence to secondary preventative cardiac medications among post-myocardial infarction (MI) patients. We implemented and assessed the effects of two hospital-based interventions-(1) standardized prolonged discharge prescription forms (90-day supply with 3 repeats for recommended cardiac medications) plus education and (2) education only-on long-term cardiac medication adherence among elderly patients post-MI. METHODS: We conducted an interrupted time series study of all post-MI patients aged 65-104 years in Ontario, Canada, discharged from hospital between September 2015 and August 2018 with ≥ 1 dispensation(s) for a statin, beta blocker, angiotensin system inhibitor, and/or secondary antiplatelet within 7 days post-discharge. The standardized prolonged discharge prescription forms plus education and education-only interventions were implemented at 2 (1,414 patients) and 4 (926 patients) non-randomly selected hospitals in September 2017 for 12 months, with all other Ontario hospitals (n = 143; 18,556 patients) comprising an external control group. The primary outcome, long-term cardiac medication adherence, was defined at the patient-level as an average proportion of days covered (over 1-year post-discharge) ≥ 80% across cardiac medication classes dispensed at their index fill. Primary outcome data were aggregated within hospital groups (intervention 1, 2, or control) to monthly proportions and independently analyzed using segmented regression to evaluate intervention effects. A process evaluation was conducted to assess intervention fidelity. RESULTS: At 12 months post-implementation, there was no statistically significant effect on long-term cardiac medication adherence for either intervention-standardized prolonged discharge prescription forms plus education (5.4%; 95% CI - 6.4%, 17.2%) or education only (1.0%; 95% CI - 28.6%, 30.6%)-over and above the counterfactual trend; similarly, no change was observed in the control group (- 0.3%; 95% CI - 3.6%, 3.1%). During the intervention period, only 10.8% of patients in the intervention groups received ≥ 90 days, on average, for cardiac medications at their index fill. CONCLUSIONS: Recognizing intervention fidelity was low at the pharmacy level, and no statistically significant post-implementation differences in adherence were found, the trends in this study-coupled with other published retrospective analyses of administrative data-support further evaluation of this simple intervention to improve long-term adherence to cardiac medications. TRIAL REGISTRATION: ClinicalTrials.gov : NCT03257579 , registered June 16, 2017 Protocol available at: https://pubmed.ncbi.nlm.nih.gov/33146624/ .

Direct Oral Anticoagulation Versus Warfarin in Patients with Atrial Fibrillation and Bioprosthetic Heart Valves: a Retrospective, Real-World Cohort Study.

OBJECTIVE: In this retrospective cohort study of consecutive patients with atrial fibrillation and surgical or transcatheter bioprosthetic valve, we compared the efficacy and safety of direct oral anticoagulants with warfarin. METHODS: Using linked health administrative databases housed at the Institute for Clinical Evaluative Sciences, we identified consecutive patients in Ontario (Canada) 65 years of age or older with AF who underwent bioprosthetic valve replacement between 1 April 2012 and 31 March 2017. We created a time-varying Cox model to examine the relationship between the type of anticoagulant and time to thrombotic or bleeding events after adjustment for baseline risk of thrombosis using the CHA2DS2-VASc score and risk of bleeding using the HAS-BLED scores. We conducted prespecified subgroup analyses according to whether valve implantation was surgical or transcatheter. RESULTS: We identified 2245 eligible patients. The mean age was 79 years, 41% were female, and 39% had transcatheter aortic valve replacement. Risk of death or thrombosis was not different between direct oral anticoagulants and warfarin after adjustment for CHA2DS2-VASc score (hazard ratio [HR] 1.02, 95% confidence interval [CI], 0.83-1.25). Risk of death or bleeding was not different between direct oral anticoagulants and warfarin after adjustment for HAS-BLED score (HR 0.89, 95% CI 0.75-1.07). Subgroup analyses of surgical or transcatheter valves were consistent with overall results. CONCLUSIONS: In a real-world population of patients with atrial fibrillation and bioprosthetic valve replacement, we found no difference between direct oral anticoagulants and warfarin with regard to the risk of thrombosis or bleeding.

Prescription patterns of outpatients and the potential of multiplexed pharmacogenomic testing.

BACKGROUND: Pre-emptive pharmacogenomic (PGx) testing is potentially an efficient approach to improve drug safety and efficacy but the target population to test is unclear. OBJECTIVES: We aim to describe the prescription pattern of PGx drugs among adult medical outpatients. METHODS: We estimated the 5-year cumulative incidence (CI) for receiving three groups of PGx drugs using competing risks analysis: (i) all PGx drugs, (ii) PGx drugs with guidelines and (iii) PGx drugs with serious clinical effects. Comparisons of CIs were also done by patient characteristics using Gray's test. RESULTS: The 5-year CIs of receiving any new PGx drug, PGx drug with guidelines and serious clinical effects were 42.6%, 37.3% and 13.7%, respectively. The 5-year CI of receiving any new PGx drug was higher for patients >40 years old (43.6% vs ≤40 years old 36.0%, P < 2.2 × 10-22 ), Malays and Indians (50.3% and 49.8% vs Chinese 31.1%, P < 2.2 × 10-22 ), those who attended one of the following four specialties at the index visit compared to other specialties (infectious diseases [46.2% vs 42.6%, P = 2.9 × 10-4 ], psychiatry [48.3% vs 42.3%, P = 7.4 × 10-13 ], renal [49.8% vs 40.9%, P < 2.2 × 10-22 ], and rheumatology and immunology [54.8% vs 41.7%, P < 2.2 × 10-22 ]) and those prescribed ≥5 drugs at index visit (51.7% vs 0-4 drugs 41.7%, P < 2.2 × 10-22 ). CONCLUSIONS: Medical outpatients have a substantial probability of benefiting from pre-emptive PGx testing and this is higher in certain subgroups of patients.

Effect of socio-demographic and health factors on the association between multimorbidity and acute care service use: population-based survey linked to health administrative data.

BACKGROUND: This study explores how socio-demographic and health factors shape the relationship between multimorbidity and one-year acute care service use (i.e., hospital, emergency department visits) in older adults in Ontario, Canada. METHODS: We linked multiple cycles (2005-2006, 2007-2008, 2009-2010, 2011-2012) of the Canadian Community Health Survey (CCHS) to health administrative data to create a cohort of adults aged 65 and older. Administrative data were used to estimate one-year service use and to identify 12 chronic conditions used to measure multimorbidity. We examined the relationship between multimorbidity and service use stratified by a range of socio-demographic and health variables available from the CCHS. Logistic and Poisson regressions were used to explore the association between multimorbidity and service use and the role of socio-demographic factors in this relationship. RESULTS: Of the 28,361 members of the study sample, 60% were between the ages of 65 and 74 years, 57% were female, 72% were non-immigrant, and over 75% lived in an urban area. Emergency department visits and hospitalizations consistently increased with the level of multimorbidity. This study did not find strong evidence of moderator or interaction effects across a range of socio-demographic factors. Stratified analyses revealed further patterns, with many being similar for both services - e.g., the odds ratios were higher at all levels of multimorbidity for men, older age groups, and those with lower household income. Rurality and immigrant status influenced emergency department use (higher in rural residents and non-immigrants) but not hospitalizations. Multimorbidity and the range of socio-demographic variables remained significant predictors of service use in the regressions. CONCLUSIONS: Strong evidence links multimorbidity with increased acute care service use. This study showed that a range of factors did not modify this relationship. Nevertheless, the factors were independently associated with acute care service use, pointing to modifiable risk factors that can be the focus of resource allocation and intervention design to reduce service use in those with multimorbidity. The study's results suggest that optimizing acute care service use in older adults requires attention to both multimorbidity and social determinants, with programs that are multifactorial and integrated across the health and social service sectors.

The impact of multimorbidity level and functional limitations on the accuracy of using self-reported survey data compared to administrative data to measure general practitioner and specialist visits in community-living adults.

BACKGROUND: Researchers often use survey data to study the effect of health and social variables on physician use, but how self-reported physician use compares to administrative data, the gold standard, in particular within the context of multimorbidity and functional limitations remains unclear. We examine whether multimorbidity and functional limitations are related to agreement between self-reported and administrative data for physician use. METHODS: Cross-sectional data from 52,854 Ontario participants of the Canadian Community Health Survey linked to administrative data were used to assess agreement on physician use. The number of general practitioner (GP) and specialist visits in the previous year was assessed using both data sources; multimorbidity and functional limitation were from self-report. RESULTS: Fewer participants self-reported GP visits (84.8%) compared to administrative data (89.1%), but more self-reported specialist visits (69.2% vs. 64.9%). Sensitivity was higher for GP visits (≥90% for all multimorbidity levels) compared to specialist visits (approximately 75% for 0 to 90% for 4+ chronic conditions). Specificity started higher for GP than specialist visits but decreased more swiftly with multimorbidity level; in both cases, specificity levels fell below 50%. Functional limitations, age and sex did not impact the patterns of sensitivity and specificity seen across level of multimorbidity. CONCLUSIONS: Countries around the world collect health surveys to inform health policy and planning, but the extent to which these are linked with administrative, or similar, data are limited. Our study illustrates the potential for misclassification of physician use in self-report data and the need for sensitivity analyses or other corrections.

Defining Patient Engagement in Research: Results of a Systematic Review and Analysis: Report of the ISPOR Patient-Centered Special Interest Group.

OBJECTIVES: Lack of clarity on the definition of "patient engagement" has been highlighted as a barrier to fully implementing patient engagement in research. This study identified themes within existing definitions related to patient engagement and proposes a consensus definition of "patient engagement in research." METHODS: A systematic review was conducted to identify definitions of patient engagement and related terms in published literature (2006-2018). Definitions were extracted and qualitatively analyzed to identify themes and characteristics. A multistakeholder approach, including academia, industry, and patient representation, was taken at all stages. A proposed definition is offered based on a synthesis of the findings. RESULTS: Of 1821 abstracts identified and screened for eligibility, 317 were selected for full-text review. Of these, 169 articles met inclusion criteria, from which 244 distinct definitions were extracted for analysis. The most frequently defined terms were: "patient-centered" (30.5%), "patient engagement" (15.5%), and "patient participation" (13.4%). The majority of definitions were specific to the healthcare delivery setting (70.5%); 11.9% were specific to research. Among the definitions of "patient engagement," the most common themes were "active process," "patient involvement," and "patient as participant." In the research setting, the top themes were "patient as partner," "patient involvement," and "active process"; these did not appear in the top 3 themes of nonresearch definitions. CONCLUSION: Distinct themes are associated with the term "patient engagement" and with engagement in the "research" setting. Based on an analysis of existing literature and review by patient, industry, and academic stakeholders, we propose a scalable consensus definition of "patient engagement in research."

BPG: Seamless, automated and interactive visualization of scientific data.

BACKGROUND: We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. RESULTS: This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. CONCLUSION: BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.

Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia.

BACKGROUND: Patients with chronic kidney disease (CKD) are at high risk of end-stage kidney disease (ESKD). The Kidney Failure Risk Equation (KFRE), which predicts ESKD risk among patients with CKD, has not been validated in primary care clinics in Southeast Asia (SEA). Therefore, we aimed to (1) evaluate the performance of existing KFRE equations, (2) recalibrate KFRE for better predictive precision, and (3) identify optimally feasible KFRE thresholds for nephrologist referral and dialysis planning in SEA. METHODS: All patients with CKD visiting nine primary care clinics from 2010 to 2013 in Singapore were included and applied 4-variable KFRE equations incorporating age, sex, estimated glomerular filtration rate (eGFR), and albumin-to-creatinine ratio (ACR). ESKD onset within two and five years were acquired via linkage to the Singapore Renal Registry. A weighted Brier score (the squared difference between observed vs predicted ESKD risks), bias (the median difference between observed vs predicted ESKD risks) and precision (the interquartile range of the bias) were used to select the best-calibrated KFRE equation. RESULTS: The recalibrated KFRE (named Recalibrated Pooled KFRE SEA) performed better than existing and other recalibrated KFRE equations in terms of having a smaller Brier score (square root: 2.8% vs. 4.0-9.3% at 5 years; 2.0% vs. 6.1-9.1% at 2 years), less bias (2.5% vs. 3.3-5.2% at 5 years; 1.8% vs. 3.2-3.6% at 2 years), and improved precision (0.5% vs. 1.7-5.2% at 5 years; 0.5% vs. 3.8-4.2% at 2 years). Area under ROC curve for the Recalibrated Pooled KFRE SEA equations were 0.94 (95% confidence interval [CI]: 0.93 to 0.95) at 5 years and 0.96 (95% CI: 0.95 to 0.97) at 2 years. The optimally feasible KFRE thresholds were > 10-16% for 5-year nephrologist referral and > 45% for 2-year dialysis planning. Using the Recalibrated Pooled KFRE SEA, an estimated 82 and 89% ESKD events were included among 10% of subjects at highest estimated risk of ESKD at 5-year and 2-year, respectively. CONCLUSIONS: The Recalibrated Pooled KFRE SEA performs better than existing KFREs and warrants implementation in primary care settings in SEA.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles.

BACKGROUND: Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. RESULTS: To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets. CONCLUSIONS: The ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

Value of diffusion-weighted and gadolinium-enhanced MRI for the diagnosis of pelvic recurrence from colorectal cancer.

PURPOSE: To prospectively assess the added value of gadolinium-enhanced and diffusion-weighted (DWI) MRI for the diagnosis of pelvic recurrence from colorectal cancer (CRC). MATERIALS AND METHODS: Fifty-two patients with suspected pelvic recurrence from CRC underwent pelvic MRI with T2-weighted ("T2"), gadolinium-enhanced fat-suppressed T1-weighted ("gadolinium") and DWI MR sequences. Three readers (senior radiologist: R1, two residents: R2, R3) scored the likelihood of recurrence on "T2," "T2 + DWI," and "T2 + Gadolinium." RESULTS: Twenty-seven patients had 42 sites of pelvic recurrence. On "T2," R1 achieved AUC of .95, sensitivity 88.4%, specificity 95.2%. For R2, these figures were .89, 81.4%, 90.5%, for R3 .90, 83.7%, 76%. Both Gadolinium injection and DWI significantly improved AUCs for residents but not for the senior radiologist: up to .988 (R2, P = 0.006) and to .98 (R3, P = 0.01) with DWI and to .96 (R2, P = 0.04), .98 (R3, P = 0.01) after gadolinium. All readers achieved slightly better AUCs with "T2 + DWI" than with "T2+Gadolinium" but not significantly (P = 0.68, P = 0.11, P = 0.3; respectively). CONCLUSION: For diagnosis of pelvic recurrence from CRC, both DWI and gadolinium-enhanced MRI significantly increase diagnostic performances compared with "T2" MRI for residents. DWI may be helpful in patients with contra-indications to intravenous administration of gadolinium.

Average Estimates in Line Graphs Are Biased Toward Areas of Higher Variability.

We investigate variability overweighting, a previously undocumented bias in line graphs, where estimates of average value are biased toward areas of higher variability in that line. We found this effect across two preregistered experiments with 140 and 420 participants. These experiments also show that the bias is reduced when using a dot encoding of the same series. We can model the bias with the average of the data series and the average of the points drawn along the line. This bias might arise because higher variability leads to stronger weighting in the average calculation, either due to the longer line segments (even though those segments contain the same number of data values) or line segments with higher variability being otherwise more visually salient. Understanding and predicting this bias is important for visualization design guidelines, recommendation systems, and tool builders, as the bias can adversely affect estimates of averages and trends.

Enhancing Emergency Department Management: A Data-Driven Approach to Detect and Predict Surge Persistence.

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77-8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises.

Specialty Psychiatric Services in US Emergency Departments and General Hospitals: Results From a Nationwide Survey.

OBJECTIVE: To explore the handling of psychiatric patients in medical hospitals and emergency departments (EDs) as well as hospital characteristics associated with the availability of psychiatric services in these settings. METHODS: From October 1, 2017, to April 1, 2018, a telephone survey regarding the presence and nature of psychiatric services was attempted among all US registered Medicare hospitals. RESULTS: Of the included 4812 US hospitals, 2394 (50%) were surveyed. Of these hospitals, 1108 (46%) have some psychiatric services available, either in medical EDs or through psychiatric consultation on general medical inpatient wards. If medical ED patients with active psychiatric issues need admission, 59% of hospitals transfer the patient to a different hospital and 28% admit the patient to a medical ward. Exploration by logistic regression analysis of the association of selected variables and available psychiatric expertise suggested that larger hospitals, nonprofit services, or hospitals in urban settings were more likely to have psychiatrists on staff or available for consultation. CONCLUSION: Despite the growing number of psychiatric patients seeking help in medical EDs and general hospitals, more than 50% of the EDs and general hospitals lack psychiatric services. These results suggest that accessibility to psychiatric care in medical settings requires improvement.

An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore.

Background: The COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes. Objective: We describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore. Materials and Methods: The study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore. Results: The models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore. Conclusion: The agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.