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36 Background: With improvements in both early detection and cancer treatment, there is a growing population of cancer survivors; with a corresponding increase in acute care use. However, models of inpatient care delivery for cancer survivors differ between hospitals and regions, which may impact resource use and outcomes. Understanding how different models influence outcomes may help define optimal models for cancer inpatient care delivery. Methods: We created a multicenter cohort of all cancer patients admitted to medical wards across 26 hospitals in Ontario, Canada from 2015 to 2022, and deterministically linked population level administrative data including ambulatory oncology data, with each hospital's patient-level electronic information (pharmacy, orders, notes, labs/imaging and results). Multivariable regression models compared characteristics and outcomes between patients admitted on oncology wards vs non-oncology wards adjusting for age, sex, income quintile, rurality, immigrant status, receiving IV systemic therapy within 120 days and comorbidity scores. Results: In total, there were 370,118 hospitalizations from 191,990 unique patients. Among these hospitalizations, 38,075 episodes (10%) were on an oncology ward. Median time from cancer diagnosis to hospitalization was 4 years; 10% received IV systemic therapy within 120 days and 16% within 1 year. The most common disease sites were GU (21%), GI (20%), breast (12%) and lung (10%). The most common discharge diagnoses from oncology wards were inpatient chemotherapy (9%), febrile neutropenia (7%), NHL (4%), AML (4%), myeloma (3%); while for non-oncology wards were heart failure (5%), palliative care (4%), UTI (2%), pneumonia (2%), acute renal failure (2%). In general, cancer patients admitted on oncology wards were younger (64 vs 76), had shorter length of stay (LOS; 9.6 vs 10.1 days), less in-hospital mortality (8% vs 11%), greater 30-day re-admission rates (30% vs 15%) and were more likely to undergo CTs (28% vs 21%), MRIs (11% vs 9%) and interventional procedures (8% vs 6%) (p<0.001, all). Subgroup analysis focusing on the top 5 discharge diagnoses from non-oncology wards, showed that despite no difference in in-hospital mortality rates (aOR 0.92 95% CI [0.58-1.46] p=0.73), admission to a non-oncology ward for those diagnoses was associated with shorter LOS (aOR 0.84 [0.78-0.90] p<0.001), reduced 30-day re-admission rates (aOR 0.60 [0.48-0.75] p<0.001), and reduced use of CTs (aOR 0.60 [0.49-0.74] p<0.001), MRIs (aOR 0.36 [0.25-0.52] p<0.001), and interventional procedures (aOR 0.43 [0.29-0.64] p<0.001). Conclusions: There are differences in resource use and outcomes for cancer survivors hospitalized on oncology versus non-oncology wards, including for patients with the same discharge diagnosis. To optimize inpatient cancer care delivery for hospitalized cancer survivors, further exploration of care models is needed. [ABSTRACT FROM AUTHOR]

Background In 2000, Ontario, Canada, initiated a universal influenza immunization program (UIIP) to provide free influenza vaccines for the entire population aged 6 mo or older. Influenza immunization increased more rapidly in younger age groups in Ontario compared to other Canadian provinces, which all maintained targeted immunization programs. We evaluated the effect of Ontario's UIIP on influenza-associated mortality, hospitalizations, emergency department (ED) use, and visits to doctors' offices. Methods and Findings Mortality and hospitalization data from 1997 to 2004 for all ten Canadian provinces were obtained from national datasets. Physician billing claims for visits to EDs and doctors' offices were obtained from provincial administrative datasets for four provinces with comprehensive data. Since outcomes coded as influenza are known to underestimate the true burden of influenza, we studied more broadly defined conditions. Hospitalizations, ED use, doctors' office visits for pneumonia and influenza, and all-cause mortality from 1997 to 2004 were modelled using Poisson regression, controlling for age, sex, province, influenza surveillance data, and temporal trends, and used to estimate the expected baseline outcome rates in the absence of influenza activity. The primary outcome was then defined as influenza-associated events, or the difference between the observed events and the expected baseline events. Changes in influenza-associated outcome rates before and after UIIP introduction in Ontario were compared to the corresponding changes in other provinces. After UIIP introduction, influenza-associated mortality decreased more in Ontario (relative rate [RR] = 0.26) than in other provinces (RR = 0.43) (ratio of RRs = 0.61, p = 0.002). Similar differences between Ontario and other provinces were observed for influenza-associated hospitalizations (RR =0.25 versus 0.44, ratio of RRs=0.58, p < 0.001), ED use (RR =0.31 versus 0.69, ratio of RRs=0.45, p < 0.001), and doctors' office visits (RR = 0.21 versus 0.52, ratio of RRs = 0.41, p < 0.001). Sensitivity analyses were carried out to assess consistency, specificity, and the presence of a dose-response relationship. Limitations of this study include the ecological study design, the nonspecific outcomes, difficulty in modeling baseline events, data quality and availability, and the inability to control for potentially important confounders. Conclusions Compared to targeted programs in other provinces, introduction of universal vaccination in Ontario in 2000 was associated with relative reductions in influenza-associated mortality and health care use. The results of this large-scale natural experiment suggest that universal vaccination may be an effective public health measure for reducing the annual burden of influenza., Introduction Annual epidemics of influenza continue to cause worldwide morbidity, mortality, and societal disruption. When there is good match to circulating strains, influenza vaccines are generally efficacious and cost-effective for [...]

Background Alcohol is a contributing cause of unintentional injuries, such as motor vehicle crashes. Prior research on the association between alcohol use and violent injury was limited to survey-based data, and the inclusion of cases from a single trauma centre, without adequate controls. Beyond these limitations was the inability of prior researchers to comprehensively capture most alcohol sales. In Ontario, most alcohol is sold through retail outlets run by the provincial government, and hospitals are financed under a provincial health care system. We assessed the risk of being hospitalized due to assault in association with retail alcohol sales across Ontario. Methods and Findings We performed a population-based case-crossover analysis of all persons aged 13 years and older hospitalized for assault in Ontario from 1 April 2002 to 1 December 2004. On the day prior to each assault case's hospitalization, the volume of alcohol sold at the store in closest proximity to the victim's home was compared to the volume of alcohol sold at the same store 7 d earlier. Conditional logistic regression analysis was used to determine the associated relative risk (RR) of assault per 1,000 I higher daily sales of alcohol. Of the 3,212 persons admitted to hospital for assault, nearly 25% were between the ages of 13 and 20 y, and 83% were male. A total of 1,150 assaults (36%) involved the use of a sharp or blunt weapon, and 1,532 (48%) arose during an unarmed brawl or fight. For every 1,000 I more of alcohol sold per store per day, the relative risk of being hospitalized for assault was 1.13 (95% confidence interval [CI] 1.02-1.26). The risk was accentuated for males (1.18, 95% CI 1.05-1.33), youth aged 13 to 20 y (1.21, 95% CI 0.99-1.46), and those in urban areas (1.19, 95% CI 1.06-1.35). Conclusions The risk of being a victim of serious assault increases with alcohol sales, especially among young urban men. Akin to reducing the risk of driving while impaired, consideration should be given to novel methods of preventing alcohol-related violence., Introduction Intentional injury is a widespread cause of death, disability, property damage, demand for emergency medical service, and grief [1]. One major precipitant is interpersonal conflict [2], whose risk factors [...]

Background: Cardiovascular symptoms in pregnancy may be a clue to psychological distress. We examined whether electrocardiogram testing in pregnant women is associated with an increased risk of subsequent postpartum depression. Methods: We conducted a population-based cohort study of pregnant women who delivered in Ontario, Canada comparing women who received a prenatal ECG to women who did not. Results: In total, 3,238,218 women gave birth during the 25-year study period of whom 157,352 (5%) received an electrocardiogram during prenatal care. Receiving an electrocardiogram test was associated with a one-third relative increase in the odds of postpartum depression (odds ratio 1.34; 95% confidence interval 1.29–1.39, p < 0.001). Conclusion: The association between prenatal electrocardiogram testing and postpartum depression suggests a possible link of organic disease with mental illness, and emphasizes that cardiovascular symptoms may be a clinical clue to the presence of an underlying mood disorder. [ABSTRACT FROM AUTHOR]

Introduction: Randomized trials recruit diverse patients, including some individuals who may be unresponsive to the treatment. Here we follow up on prior conceptual advances and introduce a specific method that does not rely on stratification analysis and that tests whether patients in the intermediate range of disease severity experience more relative benefit than patients at the extremes of disease severity (sweet spot). Methods: We contrast linear models to sigmoidal models when describing associations between disease severity and accumulating treatment benefit. The Gompertz curve is highlighted as a specific sigmoidal curve along with the Akaike information criterion (AIC) as a measure of goodness of fit. This approach is then applied to a matched analysis of a published landmark randomized trial evaluating whether implantable defibrillators reduce overall mortality in cardiac patients (n = 2,521). Results: The linear model suggested a significant survival advantage across the spectrum of increasing disease severity (β = 0.0847, P < 0.001, AIC = 2,491). Similarly, the sigmoidal model suggested a significant survival advantage across the spectrum of disease severity (α = 93, β = 4.939, γ = 0.00316, P < 0.001 for all, AIC = 1,660). The discrepancy between the 2 models indicated worse goodness of fit with a linear model compared to a sigmoidal model (AIC: 2,491 v. 1,660, P < 0.001), thereby suggesting a sweet spot in the midrange of disease severity. Model cross-validation using computational statistics also confirmed the superior goodness of fit of the sigmoidal curve with a concentration of survival benefits for patients in the midrange of disease severity. Conclusion: Systematic methods are available beyond simple stratification for identifying a sweet spot according to disease severity. The approach can assess whether some patients experience more relative benefit than other patients in a randomized trial. Highlights: Randomized trials may recruit patients at extremes of disease severity who experience less relative benefit than patients at the middle range of disease severity. We introduce a method to check for possible differential effects in a randomized trial based on the assumption that a sweet spot is related to disease severity. The method avoids a proliferation of secondary stratified analyses and can apply to a randomized trial with a continuous, binary, or censored survival primary outcome. The method can work automatically in a randomized trial and requires no additional information, data collection, special software, or investigator judgment. Such an analysis for identifying a potential sweet spot can also help check whether a negative trial correctly excludes a meaningful effect. [ABSTRACT FROM AUTHOR]