CMAJ Open

BackgroundCOVID-19 patients seen in an emergency department (ED) are at high risk of complications including post-COVID-19 condition (PCC), commonly known as Long COVID. As evidence is emerging concerning the efficacy of early post-acute rehabilitation and therapeutic interventions, early ED identification supported by a clinical prediction rule, combined with appropriate outreach and health education, could contribute to alleviating the burden of the disease on health systems and positively impact the quality of life of those living with the post-COVID-19 condition. This study aimed to derive and validate a clinical prediction rule to identify adult ED patients at high risk of developing PCC three months after an acute infection. Methods and findingsThis derivation and validation study used data from an observational cohort recruited from 33 hospitals in five Canadian provinces participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). We included adults (age [≥]18 years) with confirmed COVID-19 who presented to the ED of a participating site between October 18, 2020, and October 11, 2022. We randomly assigned participants to derivation (75%) or validation (25%) datasets, and prespecified clinical variables as candidate predictors. We used a fast step-down logistic regression to reduce the model to key predictors for our clinical prediction rule. Validation was planned only if the derived rule had an AUC of at least 80% to support clinically useful discrimination characteristics to separate those who will develop PCC from those who will not. Of 6,070 eligible patients, 2,511 (41.4%) reported PCC symptoms at three months. Our derived clinical prediction rule included nine risk factors (female sex, higher arrival respiratory rate, comorbidities (rheumatologic disorder and mental health condition), acute symptoms (sputum production, dizziness, diarrhea, chest pain, and fatigue)) and one protective factor (self-reported South Asian race). In derivation, the optimism-corrected area under the curve was 0.626 (95% confidence interval [CI] 0.610-0.643). Age and vaccination status were not retained in the final clinical prediction rule. The rule was only slightly better than chance and deemed not accurate enough to meaningfully guide decision-making in the ED. Therefore, we did not proceed to examine its performance in the validation cohort. ConclusionsDespite rigorous methodology, we were unable to derive a clinical prediction rule with sufficient accuracy to predict PCC in emergency department patients at the time of the acute infection. However, we did identify several factors associated with the development of PCC that can guide future studies about the causes of PCC. The ambiguous nature of the current PCC diagnostic criteria and the extended follow-up pose challenges for deriving a useful clinical decision rule. Further research integrating comprehensive surveillance systems and biomarker data may also enhance prediction accuracy and refine personalized management strategies in the emergency department setting.

BackgroundUse of masks and respirators for prevention of respiratory infectious disease transmission is not new, but has proven controversial, and even politically polarizing during the SARS-CoV-2 pandemic. In the Canadian province of Ontario, mask mandates were introduced by the 34 regional health authorities in an irregular fashion from June to September 2020, creating a quasi-experiment that can be used to evaluate impact of community mask mandates. Ontario SARS-CoV-2 case counts were strongly biased by testing focussed on long-term care facilities and healthcare workers. We developed a simple regression-based test-adjustment method that allowed us to adjust cases for undertesting by age and gender. We used this test- adjusted time series to evaluate mask mandate effectiveness. MethodsWe evaluated the effect of masking using count-based regression models that allowed adjustment for age, sex, public health region and time trends with either reported (unadjusted) cases, or testing-adjusted case counts, as dependent variables. Mask mandates were assumed to take effect in the week after their introduction. Model based estimates of effectiveness were used to estimate the fraction of SARS- CoV-2 cases, severe outcomes, and costs, averted by mask mandates. ResultsModels that used unadjusted cases as dependent variable identified protective effects of masking (effectiveness 15-42%), though effectiveness was variably statistically significant, depending on model choice. Mask effectiveness in models predicting test-adjusted case counts was substantially higher, ranging from 49% (44- 53%) to 73% (48-86%) depending on model choice. Effectiveness was greater in women than men (P = 0.016), and in urban health units as compared to rural units (P < 0.001). The prevented fraction associated with mask mandates was 46% (41-51%), averting approximately 290,000 clinical cases, averting 3008 deaths and loss of 29,038 QALY. Costs averted represented $CDN 610 million in economic wealth. ConclusionsLack of adjustment for SARS-CoV-2 undertesting in younger individuals and males generated biased estimates of infection risk and obscures the impact of public health preventive measures. After adjustment for under-testing, the effectiveness of mask mandates emerges as substantial, and robust regardless of model choice. Mask mandates saved substantial numbers of lives, and prevented economic costs, during the SARS-CoV-2 pandemic in Ontario, Canada.

BackgroundThe COVID-19 pandemic placed immense strain on Canadas healthcare system and disproportionately affected individuals with poorer baseline health. Healthcare-associated infections (HAIs) increase risk for both patients and healthcare workers and are often more severe due to advanced age and comorbidities. While efforts have aimed to reduce in-hospital transmission, the individual- and community-level consequences of HAIs require further study. We aimed to assess whether healthcare-associated COVID-19 cases had higher odds of death compared to hospitalized community-acquired cases, and to evaluate the directionality of transmission between hospitals and the community. MethodsWe analyzed COVID-19 surveillance data from Ontarios Case Contact and Management System and the COVaxON vaccine registry (March 17, 2020, to September 4, 2022). Latent class analysis was used to classify hospitalized cases by likelihood of healthcare-associated infection. Mortality odds by category were estimated using binomial logistic regression. Directionality between hospital outbreaks and community cases was assessed using a modified Granger causality approach. FindingsCompared to patients with low likelihood of healthcare-associated infection, those moderately likely to have acquired COVID-19 in hospital had elevated odds of death (OR: 1.26, 95% CI: 1.14-1.40); no significant increase was seen in the high-likelihood group (OR: 1.05, 95% CI: 0.96-1.15). Community cases did not predict hospital outbreaks (p=0.5749), but hospital outbreaks predicted community case growth (p<0.0001). InterpretationHospital-acquired COVID-19 is associated with excess mortality and may drive community transmission. Preventing in-hospital transmission is critical to protecting patients and controlling broader epidemic spread. FundingSupported by a Canadian Institutes for Health Research project grant, #518192.

As the COVID-19 pandemic has progressed, more local data has become available, enabling a more granular modeling approach. In March 2020, we developed a COVID-19 Resource Estimator (CORE) model to estimate the acute care resource use in Ontario, Canada. In this paper, we describe the evolution of CORE2.0 to incorporate age, sex, and time-dependent acute care resource use, length of stay, and mortality to simulate hospital occupancy. Demographics (e.g., age and sex) of infected cases are informed by 4-month averages between March-June, and July-October using 10-year age groups. The probability of hospitalization, ICU admission, and requiring mechanical ventilation are all age and sex-dependent. LOS for each acute care level ranges from 5.7 to 16.15 days in the ward, 6.5 to 10.7 days in the ICU without ventilation, and 14.8 to 21.6 days on the ventilator, depending on month of infection. We calibrated some LOS components to reported ward and ICU occupancy between June 15 and October 31, 2020. Furthermore, we demonstrate the use of CORE2.0 for a regional analysis of Region of Waterloo, Ontario, Canada to simulate the ward bed, ICU bed, and ventilator occupancies for 30 days starting December 2020 for three case trajectory scenarios. Moving forward, this model has become highly flexible and customizable to data updates, and can better inform acute care planning and public measures as the pandemic progresses.

This study combined public data and geospatial analysis to examine physicians language abilities and locations in Alberta, Canada, and produced an interactive map to allow patients and policymakers to view the data. We identified n=11,370 active physicians in the province of Alberta, of whom we further identified n=194 (1.7%) as University of Ottawa (uOttawa) graduates, n=955 (8.4%) as French-speaking, and n=4,965 (43.7%) as community-based family physicians. French-speaking physicians were concentrated in Census Division 6 (n=464, 48.6%) surrounding Calgary and Census Division 11 (n=356, 37.3%) surrounding Edmonton. Overall reported French-language ability was low, with just 955 (8.4%) of all active physicians reporting competency in French. uOttawa graduates (n=70, 36.1%) were much more likely to report French ability than graduates of other schools (n=885, 7.9%), women (n=457, 9.6%) were slightly more likely than men (n=497, 7.6%), and specialists (n=666, 10.4%) were more likely than family physicians (n=289, 5.8%).

BackgroundHaving temporary immigration status affords limited rights, workplace protections, and access to services. There is not yet research data on impacts of the COVID-19 pandemic for people with temporary immigration status in Canada. MethodsWe use linked administrative data to describe SARS-CoV-2 testing, positive tests, and COVID-19 primary care service use in British Columbia from January 1, 2020, to July 31, 2021, stratified by immigration status (Citizen, Permanent Resident, Temporary Resident). We plot the rate of people tested and the rate of people confirmed positive for COVID-19 by week from April 19, 2020, to July 31, 2021, across immigration groups. Results4.9% of people with temporary immigration status had a positive test for SARS-CoV-2 over this period, compared to 4.0% among people with permanent residency and 2.1% among people who hold Canadian citizenship. This pattern is persistent by sex/gender, age group, neighborhood income quintile, health authority, and in both metropolitan and small urban settings. At the same time we observe lower access to testing and COVID-19 related primary care among people with temporary status. InterpretationPeople with temporary immigration status in BC experience higher SARS-CoV-2 test positivity; alarmingly, this was coupled with lower access to testing and primary care. Interwoven immigration, health and occupational policies place people with temporary status in circumstances of precarity and higher health risk. Extending permanent residency status to all immigrants residing in Canada and decoupling access to health care from immigration status could reduce precarity due to temporary immigration status.

Background: The SARS-CoV-2 disease 2019 (COVID-19) pandemic has spread across the world with varying impact on health systems and outcomes. We assessed how the type and timing of public-health interventions impacted the course of the outbreak in Alberta and the other Canadian provinces. Methods: We used publicly-available data to summarize rates of laboratory data and mortality in relation to measures implemented to contain the outbreak and testing strategy. We estimated the transmission potential of SARS-CoV-2 before the state of emergency declaration for each province (R0) and at the study end date (Rt). Results: The first cases were confirmed in Ontario (January 25) and British Columbia (January 28). All provinces implemented the same health-policy measures between March 12 and March 30. Alberta had a higher percentage of the population tested (3.8%) and a lower mortality rate (3/100,000) than Ontario (2.6%; 11/100,000) or Quebec (3.1%; 31/100,000). British Columbia tested fewer people (1.7%) and had similar mortality as Alberta. Data on provincial testing strategies were insufficient to inform further analyses. Mortality rates increased with increasing rates of lab-confirmed cases in Ontario and Quebec, but not in Alberta. Ro was similar across all provinces, but varied widely from 2.6 (95% confidence intervals 1.9-3.4) to 6.4 (4.3-8.5), depending on the assumed time interval between onset of symptoms in a primary and a secondary case (serial interval). The outbreak is currently under control in Alberta, British Columbia and Nova Scotia (Rt <1). Interpretation: COVID-19-related health outcomes varied by province despite rapid implementation of similar health-policy interventions across Canada. Insufficient information about provincial testing strategies and a lack of primary data on serial interval are major limitations of existing data on the Canadian COVID-19 outbreak.

ObjectivesEmergency Medical Services (EMS) demand is increasing, with a growing proportion of low-acuity encounters. Prior studies are limited by regional sampling, inconsistent definitions, narrow observation periods, and limited theoretical grounding. The objective of this study was to identify predisposing, enabling, and need-based factors associated with EMS transport among low-acuity emergency department (ED) patients, guided by Andersens Behavioral Model of Health Services Use. MethodsWe conducted a secondary, retrospective observational study using a 10% random sample of multicenter electronic health record (EHR) data from 21 emergency departments in the southeastern United States. To be eligible to be included in the analysis, the visit had to be for: (1) patient age >17 years of age, (2) occur between January 1, 2016, to April 29, 2025, (3) triaged as Emergency Severity Index (ESI) 4 or 5, and (4) ended in a final visit disposition of being discharged. The primary outcome was EMS utilization. Independent variables were categorized as predisposing, enabling, or need-based factors according to Andersens model. We used multivariable logistic regression to estimate adjusted odds ratios (aORs) with 95% confidence intervals. ResultsAmong 41,772 low-acuity ED encounters, 3,233 (7.7%) arrived by EMS. Increased odds of EMS use were associated with older age (per 10-year increase; aOR 1.30, 95% CI=1.27-1.33), male sex (aOR 1.20, 95% CI=1.12-1.30), being retired or disabled (aOR 3.60, 95% CI=3.15-4.10), being unemployed (aOR 2.26, 95% CI=2.04-2.52), having a nighttime presentation (aOR 1.63, 95% CI=1.51-1.76), and mental health diagnosis (aOR 1.76, 95% CI=1.62-1.90). Protective factors included White race (aOR=0.89, 95% CI=0.83-0.96), established primary care (aOR=0.57, 95% CI=0.57-0.62), weekend presentation (aOR 0.91; CI = 0.84-0.99), and visits during (aOR 0.63, 95% CI=0.55-0.71) or after (aOR 0.54, 95% CI=0.48-0.61) the COVID-19 period. Rurality, insurance, and primary language were not associated with EMS use. ConclusionsPredisposing and enabling factors were the predominant drivers of low-acuity EMS utilization in this sample. Expanding access to primary care and behavioral health services, especially for older patients, may reduce EMS use for low-acuity complaints while preserving EMS capacity for higher-acuity emergencies.

The pandemic caused by SARS-CoV-2 has proven challenging clinically, and at the population level, due to heterogeneity in both transmissibility and severity. Recent case incidence in Ontario, Canada (autumn 2020) has outstripped incidence in seen during the first (spring) pandemic wave; but has been associated with a lower incidence of intensive care unit (ICU) admissions and deaths. We hypothesized that differential ICU burden might be explained by increased testing volumes, as well as the shift in mean case age from older to younger. We constructed a negative binomial regression model using only three covariates, at a 2-week lag: log10(weekly cases); log10(weekly deaths); and mean weekly case age. This model reproduced observed ICU admission volumes, and demonstrated good preliminary predictive validity. Furthermore, when admissions were used in combination with ICU length of stay, our modeled estimates demonstrated excellent convergent validity with ICU occupancy data reported by the Canadian Institute for Health Information. Our approach needs external validation in other settings and at larger and smaller geographic scales, but appears to be a useful short-term forecasting tool for ICU resource demand; we also demonstrate that the virulence of SARS-CoV-2 infection has not meaningfully changed in Ontario between the first and second waves, but the demographics of those infected, and the fraction of cases identified, have.

AbstractO_ST_ABSObjectivesC_ST_ABSCompare the average cost of an emergency department (ED) visit between three ED care models, namely management by an emergency physician (EP) alone (usual care), management by a primary contact physiotherapist (PT) and an EP (intervention), and management by a PT alone (sensitivity analysis). MethodsCost study (Canadian Public Payer perspective) based on data collected during a pragmatic randomized clinical trial (2018-2019) conducted in an urban Canadian academic ED (CHUL, Quebec City, Canada; n=78, 18-80 years old). Costs incurred for the management of persons presenting to the ED for a minor musculoskeletal disorder (MSKD) were calculated using Time-Driven Activity-Based Costing, in which time invested with a patient determines care costs. The main outcome measure was the average cost of an ED visit. Generalized linear models with Gamma distributions and log links were used to assess whether there were significant differences in average costs between the care models. ResultsMean ED visit cost was $267.08 (2019 $CAD, 95%CI: $212.75, $346.40) for PT and EP management, compared with $245.14 for EP management ($169.46, $336.72), resulting in a non- significant absolute difference of 21.94 CAD/patient ($-87.33, $132.63) between models (p=.60). Sensitivity analyses showed that the average cost of ED management by a PT was $194.38 ($161.50, $234.34), representing a non-significant average saving of 50.76 CAD/patient ($- 156.91, $37.54) compared to EP management. ConclusionThis study is a first step towards a better understanding of the costs incurred by the Canadian Public Payer for the management of persons presenting with MSKDs in the ED. Primary contact physiotherapists have the potential to complement care of MSKD ED patients without increasing healthcare costs.

PurposeEmergency department (ED) patients suffering from acute respiratory infection or infectious diarrhea often present with self-limiting conditions. The study objective was to evaluate the performance of triage clinical decision rules consisting of a rapid molecular test and a self-administered patient questionnaire to identify ED patients who can self-treat at home without consulting an emergency physician. This article describes the profile of the cohorts recruited. ParticipantsParticipants were prospectively recruited in 4 EDs in Quebec City and Montreal, Canada, from February 2022 through March 2023. Participants were aged [&ge;]18 years, had an acute respiratory infection and/or acute infectious diarrhea, and had received a Canadian Triage and Acuity Scale score between 3 (urgent) and 5 (non-urgent). Participants were asked to complete a self-administered risk stratification questionnaire after triage and to follow usual ED care afterward. Nasopharyngeal and/or rectal swabs were collected and frozen for subsequent testing on a rapid molecular testing device. Data were obtained during the recruitment visit, during a follow-up phone call 7 days later and from medical records. The primary outcome to be predicted by the clinical decision rules was an aggregation of hospitalization, return visit and mortality at 7 days. Findings to dateWe recruited 1,391 participants, 62.3% of whom were women, 80.7% were aged under 60, 78.2% had no comorbidities, 76.5% presented with an acute respiratory infection, 17.8% with an acute infectious diarrhea and 5.7% with both. Hospitalization and return visits incidence proportions at 7 days were respectively 10.8% and 13.1% for respiratory infections and 14.1% and 16.5% for infectious diarrhea. No death was recorded. Future plansThe data gathered from these cohorts will enable us to test, refine, derive, and validate clinical decision rules used to help ED triage nurses offer the most suitable care to patients presenting with acute respiratory infections or infectious diarrhea. Strengths and limitationsOur study has both strengths and limitations. Among the strengths: O_LIThe cohorts were recruited from 4 different EDs and reached the target sample size for acute respiratory infections and acute infectious diarrhea. C_LIO_LIThe potential economic impact of the clinical decision rules will be assessed from the perspective of both the health system and the patient. C_LI The main limitations are the following. O_LICohorts were recruited by convenience sampling and may not be representative of the entire ED population. C_LIO_LIThe patient self-administered questionnaires used in this study were derived from systematic reviews and rapid prototyping, but not according to the methodological standards recommended for the derivation of clinical decision rules. However, the study dataset was built to enable rules to be refined and if necessary, new rules to be derived and internally validated. C_LIO_LIWe recorded a 12.9% loss of participants at the 7-day follow-up phone call. However, the primary outcome measures (return visits, admissions and deaths) will be obtained from provincial administrative databases. These reliable data will enable us to overcome this limitation for future projects to refine and validate robust triage clinical decision rules. C_LI

BackgroundThe requirement for critical care in even a modest fraction of SARS-CoV-2 infected individuals made ICU resources an important societal chokepoint during the recent pandemic. We developed a simple regression-based point score in 2020 based on an objective of forecasting critical care occupancy in the Canadian province of Ontario based on mean age of cases, case numbers, and testing volume. Evolution of the pandemic (variants of concern, vaccination) led us to re-assess and re-calibrate our earlier work, with inclusion of information vaccination which became widespread in 2021. MethodsWe obtained complete provincial SARS-CoV-2 case, testing, and vaccination data for the period from March 2020 to September 2022, with data subdivided into 6 major "waves", following the approach applied by other Canadian investigators. Our initial model was fit only using the first two "wild type" SARS-CoV-2 waves; an updated model included wave 3 (N501Y+ variants). Our model was validated by comparing model projections to waves not used for model fitting; validation model fits were evaluated with Spearmans rho; counterfactuals without vaccination were modeled to impute fraction of critical care admissions prevented with vaccination. Costing was based on published economic estimates. ResultsOur initial model (fit to waves 1 and 2) was well calibrated (rho 0.85) but predictive validity was modest (rho 0.46). Predictive validity improved in models fit to the first 3 pandemic waves without vaccination (rho 0.60) or with vaccination (rho 0.68) (P for inclusion of vaccination 0.013 by Likelihood Ratio Test). Prevented fraction of ICU admissions attributable to vaccination was 144% (22017 admissions expected vs. 9020 observed); based on published estimates of ICU admission cost for SARS-CoV-2 the 12977 admissions averted $2.9 (CDN) billion in economic costs, in contrast to the $3 billion total cost of the vaccination program. ConclusionsSimple time series regression incorporating case and testing characteristics continues to be useful as a tool for forecasting critical care occupancy due to SARS-CoV-2 but early pandemic models need to be updated to capture the preventive effects of widespread vaccination. The economic benefit of vaccination for prevention of critical care resource consumption during the pandemic is substantial, achieving near cost neutrality with the provinces entire vaccination program.

IntroductionNational responses to the SARS-CoV-2 pandemic have been highly variable, which may explain some of the heterogeneity in the pandemics health and economic impacts across the world. We sought to explore the effectiveness of the Canadian pandemic response relative to responses in four peer countries with similar political, economic and health systems, and with close historical and cultural ties to Canada (the United States, United Kingdom, France, and Australia) from March 2020 to May 2022. MethodsWe used reported age-specific mortality data to generate estimates of pandemic mortality standardized to the Canadian population. Age-specific case fatality, hospitalization, and intensive care admission probabilities for the Canadian province of Ontario were applied to estimated deaths in order to calculate hospitalizations and intensive care admissions averted by the Canadian response. The monetary value of averted hospitalizations was estimated using cost estimates from the Canadian Institute for Health Information. Age-specific quality-adjusted life-years (QALY) lost due to fatality were estimated using published estimates. QALY were monetized using a net expected benefit approach. ResultsRelative to the United States, United Kingdom, and France, the Canadian pandemic response was estimated to have averted 94,492, 64,306 and 13,641 deaths respectively, with more than 480,000 hospitalizations averted, and 1 million QALY saved, relative to the United States. A United States pandemic response applied to Canada would have resulted in more than $40 billion in economic losses due to healthcare expenditures and lost QALY; losses relative to the United Kingdom and France would have been $21 billion and $5 billion respectively. By contrast, an Australian pandemic response would have averted over 28,000 additional deaths and averted nearly $9 billion in costs in Canada. ConclusionsCanada outperformed peer countries that aimed for mitigation, rather than elimination, of SARS-CoV-2 in the first two years of the pandemic, likely because of a more stringent public health response to disease transmission. This resulted in substantial numbers of lives saved and economic costs averted. However, comparison with Australia demonstrates that an elimination focus would have allowed Canada to save tens of thousands of lives, and would have saved substantial economic costs.

BackgroundAs of December 30, 2021, Ontario long-term care (LTC) residents who received a third dose of COVID-19 vaccine [&ge;]84 days previously were offered a fourth dose to prevent a surge in COVID-19-related morbidity and mortality due to the Omicron variant. Seven months have passed since fourth doses were implemented, allowing for the examination of fourth dose protection over time. MethodsWe used a test-negative design and linked databases to estimate the marginal effectiveness (4 versus 3 doses) and vaccine effectiveness (VE; 2, 3, or 4 doses versus no doses) of mRNA vaccines among Ontario LTC residents aged [&ge;]60 years who were tested for SARS-CoV-2 between December 30, 2021 and August 3, 2022. Outcome measures included any Omicron infection, symptomatic infection, and severe outcomes (hospitalization or death). ResultsWe included 21,275 Omicron cases and 273,466 test-negative controls. The marginal effectiveness of a fourth dose <84 days ago compared to a third dose received [&ge;]84 days ago was 23% (95% Confidence Interval [CI] 17-29%), 36% (95%CI 26-44%), and 37% (95%CI 24-48%) against SARS-CoV-2 infection, symptomatic infection, and severe outcomes, respectively. Additional protection provided by a fourth dose compared to a third dose was negligible against all outcomes [&ge;]168 days after vaccination. Compared to unvaccinated individuals, vaccine effectiveness (VE) of a fourth dose decreased from 49% (95%CI 44%-54%) to 18% (95%CI 5-28%) against infection, 69% (95%CI 62-75%) to 44% (95%CI 24-59%) against symptomatic infection, and 82% (95%CI 77-86%) to 74% (95%CI 62-82%) against severe outcomes <84 days versus [&ge;]168 days after vaccination. ConclusionsOur findings suggest that fourth doses of mRNA COVID-19 vaccines provide additional protection against Omicron-related outcomes in LTC residents, but the protection wanes over time, with more waning seen against infection than severe outcomes.

IntroductionA physician handoff is the process through which physicians transfer the primary responsibility of a care unit. The emergency department (ED) is a fast-paced and crowded environment where the risk of information loss between shifts is significant. Yet, the impact of handoffs between emergency physicians on patient outcomes remains understudied. We performed a retrospective cohort study in the ED to determine if handed-off patients, when compared to non-handed-off patients, were at higher risk of negative outcomes. MethodsWe included every adult patient first assessed by an emergency physician and subsequently admitted to hospital in one of the five sites of the CHU de Quebec-Universite Laval during fiscal year 2016-17. Primary outcome was mortality. Secondary outcomes were incidence of ICU admission and surgery and hospital length of stay. We conducted propensity score based analysis accounting for patient and hospital clusters and adjusting for demographics, multiple disease severity indicators and ED processes indicators, including crowding. Results21,136 ED visits and 17,150 unique individuals were included in the study. Median[Q1-Q3] age, Charlson score, door-to-emergency-physician time and ED length of stay were 71[55-83] years old, 3[1-4], 48 [24,90] minutes, 20.8[9.9,32.7] hours, respectively. In propensity score analysis (OR handoff/no handoff [CI95%] or GMR[Cl95%]), handoff status was not associated with mortality (1.08[0.93,1.26]), ICU admission (1.01[0.87,1.18]) or hospital length of stay (1.02[0.94-1.10]). Sensitivity and sub-group based analyses yielded no further information. ConclusionEmergency physicians handoffs were not associated with an increase in risk of severe in-hospital adverse events. Further studies are needed to explore the impact of ED handoffs on adverse events of low and moderate severity. What is already known on this subjectHandoffs are widely believed to affect patient outcomes in the ED, although data remains scarce. What did this study askWhat is the impact of handoffs between emergency physicians on patient outcomes What this study addsHanded off patients do not seem to do worse than non handed off patients concerning majors outcomes (mortality, ICU admission, LOS). Why does it matter to clinicians?In the ED, handoffs may not represent a risk to patient outcomes. QI efforts may be better invested in other care inefficiencies.

The rapid spread of SARS-CoV-2 in early 2020 forced provincial health authorities across Canada to quickly institute infection control measures. It is now four years since the start of the global pandemic, and an opportune time to consider how Canadas provinces compared in their SARS-CoV-2 containment policies and the resulting impacts on mortality and economic activity. I compare provincial exposure to SARS-CoV-2 using data on the number out-of-province arrivals into each province. I compare the key containment measures used in each province, the length of time that these measures were imposed, and the uptake of COVID-19 vaccines by province. Using Statistics Canada data, I also estimate the impact of the COVID-19 pandemic on provincial crude death rates during 2020-2023, and life expectancy and gross domestic product during 2020-2022. I find substantial provincial variation in pandemic responses and outcomes. The provinces varied in their use of the most stringent public health measures. Uptake of the primary COVID vaccinations varied from 76% to 92%; booster vaccination uptake varied even more. There was also marked provincial variation in mortality and economic outcomes. While this study does not estimate the impacts of provincial response stringency and COVID vaccine uptake on mortality and other outcomes, it does provide suggestive evidence that can be formally assessed in future research.

OBJECTIVES: Population aging is a major contributor to increasing demand for emergency medical services (EMS), yet EMS workforce projections based on population data remain limited. This study projected future EMS incident volume and clinician workforce requirements in Florida from 2026 to 2035 based on historical data on EMS response records to inform workforce planning. METHODS: We conducted a retrospective, population-based secondary analysis and forecasting study using de-identified state-wide emergency EMS response records from Florida's Emergency Medical Services Tracking and Reporting System (EMSTARS) spanning January 1, 2017 through December 31, 2025. Incidents were assigned to seven age cohorts and aggregated into monthly time series. We used Seasonal Autoregressive Integrated Moving Average models with exogenous inputs (SARIMAX) to project age and cohort-specific incident volume for 2026 through 2035. Projected future incident volumes were translated into EMT and paramedic full-time equivalent (FTE) requirements using observed EMSTARS staffing configurations and target operational parameters. RESULTS: Annual EMS incidents increased from 4.10 million in 2017 to 5.22 million in 2025 and are projected to reach 7.76 million by 2035, a 48.8% increase over the 2025 baseline. By 2035, adults aged 60 and older are projected to represent 31.2% of Florida's population while accounting for 61.6% of all EMS incidents. Total estimated EMS workforce requirements are projected to increase from 9,542 FTEs in 2025 to 14,195 FTEs by 2035, requiring approximately 4,654 additional FTEs (a 48.8% increase). CONCLUSIONS: Florida's aging population is projected to drive a nearly 50% increase in EMS incident volume and associated workforce requirements over the next decade, with demand disproportionately concentrated among older adults. With a substantial concentration of adults aged 80 and older and a rapidly expanding oldest-old cohort, Florida is confronting the demographic conditions projected to emerge in other states over the next decade. The findings offer researchers and policymakers a replicable framework and a directly applicable planning reference for jurisdictions across the United States.

BackgroundThe burden of COVID-19 in Canada is unequally distributed geographically, with the largest number of cases and fatalities recorded in Quebec and Ontario while other provinces experienced limited outbreaks. To date, however, no study has assessed how provincial epidemics have unfolded in a comparative perspective. This is essential to calibrate projections of the future course of the epidemic and plan health care resources for the second wave of infections. MethodsUsing newly released individual-level data collected by the Public Health Agency of Canada, we assess COVID-19-related morbidity and mortality across age and gender groups at the provincial level through a combination of demographic and survival analyses. ResultsQuebec has the highest absolute and per capita number of COVID-19 confirmed positive cases, hospitalizations and fatalities in all age groups. In each province, a higher number of women than men test positive for the disease, especially above age 80. Yet consistently across age groups, infected men are more likely to be hospitalized and enter intensive care than women do. These gender differences in hospitalisation rates account for the higher case fatality risk due to COVID-19 among men compared to women. InterpretationAlthough health care capacity across provinces has been sufficient to treat severe cases, we find that the main factor accounting for gender differences in COVID-19-related mortality is the need for hospitalization and intensive care, especially above age 80. This suggests a selection effect of severe cases requiring to be treated in a hospital setting that needs to be further investigated.

ImportanceMissed opportunities for diagnosis (MODs), sometimes termed diagnostic errors, are a major cause of patient morbidity and mortality in the emergency department (ED). EDs have employed eTriggers, rule-based case collections likely to have a higher than average error rate (e.g. 72 hour returns with admission), but their utility is limited by low error yields. Large language models (LLMs) offer new opportunities to identify MODs and contribute to both individual- and systems-level quality improvement. ObjectiveTo determine whether sequential screening of ED cases with eTriggers and an LLM can more efficiently identify MODs compared to eTriggers alone. DesignRetrospective observational cohort study of ED encounters collected between March 2015 and June 2025. Setting10 EDs (2 academic, 8 community) in a single US health system. ParticipantsEmergency physicians reviewed and adjudicated random samples of cases identified by 3 previously validated eTriggers (72-hour return with admission, 10-day return with ICU admission, and floor-to-ICU escalation within 24 hours) using the SaferDX instrument. An ED physician also evaluated a novel hybrid eTrigger combining an LLM adjudicator with a rules engine for 9-day return admissions with emergency care- sensitive conditions (ECSCs). ExposuresLLM MOD adjudication of ED cases with Claude Sonnet 4 using an iteratively-developed, standardized prompt incorporating the SaferDx instrument. Main Outcome(s) and Measure(s)Positive predictive value (PPV), sensitivity, specificity, negative predictive value (NPV), and number needed to screen (NNS) for MODs. Reviewer time to adjudicate cases and quality improvement stakeholder assessments of LLM case summaries were also measured. ResultsOf the 357 encounters (mean [SD] age, 65.2 [17.8] years; 47.1% female) reviewed, adjudicated MOD PPV ranged from 11.0% to 18.6% across traditional eTriggers. For 72-hour return admissions, the LLM achieved sensitivity 85.7% (95% CI, 65.4%-95.0%), specificity 56.8% (95% CI, 49.3%-64.0%), PPV 19.8%, and NPV 97.0%. For 10-day ICU returns, sensitivity was 100% (95% CI, 56.6%-100%), specificity 43.5% (95% CI, 25.6%-63.2%), PPV 27.8%, and NPV 100%. For floor-to-ICU escalations, sensitivity was 55.6% (95% CI, 33.7%-75.4%), specificity 64.6% (95% CI, 53.6%-74.2%), PPV 26.3%, and NPV 86.4%. The hybrid ECSC eTrigger identified 110 MODs (53.1% of 207 encounters), with blinded review of a stratified sample estimating PPV 45% and NPV 100%. Expert reviewers required a median of 5 minutes per case; restricting review to LLM-positive charts reduced review time by up to 50% without missed errors for these triggers. In stakeholder review, LLM-generated case summaries were rated highly actionable for individual clinician feedback (mean, 4.1 of 5) but less so for systems-level interventions (mean, 1.4 of 5). Conclusions and RelevanceIn this multisite retrospective study, LLMs demonstrated high NPVs across multiple eTrigger criteria. Sequential use of LLM and human review improved efficiency and detection compared with traditional eTriggers, and narrative case summaries offered a novel method to identify opportunities for clinician-level feedback. These findings suggest that LLM-based approaches may provide scalable diagnostic quality oversight in the ED. Key PointsO_ST_ABSQuestionC_ST_ABSCan sequential screening with eTriggers and a large-language-model (LLM) identify missed opportunities for diagnosis (MODs) in the emergency department, improving screening efficiency versus traditional eTriggers? FindingsIn a multicenter retrospective cohort (10 EDs; 317 reviewed encounters), LLM adjudication showed high sensitivity and NPV across three established eTriggers (e.g., 72-hour returns: sensitivity 85.7%, NPV 97.0; 10-day ICU returns: sensitivity 100%, NPV 100%). A sequential approach was validated on a novel eTrigger for 9-day returns for select emergency care sensitive conditions, achieving PPV 45% and NPV 100% in 40 blinded samples. MeaningLLM-augmented eTrigger screening offers scalable, efficient MOD detection to support diagnostic quality oversight in EDs.

BackgroundUnmet social needs can increase the risk of adverse health outcomes in children. Emergency medical services (EMS) utilization is higher among families facing challenges with certain social determinants of health (SDoH). Understanding service needs among families seeking emergency care may inform interventions designed to support better health outcomes. MethodsWe conducted a cross-sectional survey of caregivers and EMS clinicians presenting to a pediatric emergency department (ED) via EMS or private vehicle (POV). Participants completed a survey reporting access to transportation, food, housing, healthcare, mental healthcare, and financial support. We evaluated differences in self-reported SDoH and service needs by arrival mode and differences in caregiver-reported needs with those perceived by EMS clinicians using Pearsons chi-squared and Fishers exact tests. ResultsA total of 159 caregivers (111 POV, 48 EMS) and 17 EMS clinicians were enrolled. Caregiver demographics were similar between groups. Overall, 67 (42%) caregivers reported at least one service need with few differences between groups. Difficulty seeking healthcare, childcare, and mental health support were most frequently reported. EMS arrivals were more likely to report difficulty seeking child healthcare (p=.02); POV arrivals were more likely to report personal mental health needs (p<.01). There was no difference in requests for child mental health treatment (p>.90). ConclusionsService needs are common in pediatric emergency care. Difficulty accessing child healthcare and mental healthcare were common in our population. Screening pediatric caregivers during emergency care including during EMS activations may identify families in need of support services. KEY MESSAGE STATEMENTSocial Determinants of Health (SDoH) have important health implications in children. In pediatric emergency care, caregivers and children arriving via Emergency Medical Services (EMS) are more likely to have social needs. There is a paucity of research on the specific needs of this population compared to private vehicle arrives and if EMS clinicians are aware of these needs. This study compares the SDoH of caregivers in the ED between arrival type and assesses if EMS clinicians recognize the needs of the people they transport. These results may be useful in improving EMS clinician curricula and addressing SDoH in families with young children.