ICES (formerly known as the Institute for Clinical Evaluative Sciences) is an independent, non-profit corporation that applies the study of health informatics for health services research and population-wide health outcomes research in Ontario, Canada, using data collected through the routine administration of Ontario's system of publicly funded health care. ICES scientists have secure access to Ontario's health administrative data. ICES research teams produce peer-reviewed scientific journal articles, as well as reports and atlases to assist health care providers, government planners and policy makers in improving population health through the advancement of evidence-based practice and health policy.ICES was established in 1992 and is governed by a Board of Directors. ICES receives core funding from the Ontario Ministry of Health and Long-Term Care (MOHLTC). In addition, ICES faculty and staff receive peer-reviewed grants from federal funding agencies such as the Canadian Institutes of Health Research, and project-specific funds from provincial and national organizations.ICES' central location is on the campus of Sunnybrook Health Sciences Centre in Toronto, with satellite locations in Kingston, Ontario, London, Ontario, Hamilton, Ontario and Sudbury, Ontario.
Anti-CD19 Chimeric Antigen Receptor T-cell (CAR-T) immunotherapies have been funded in Canada for relapsed-refractory large B-cell lymphoma (RR-LBCL) after two lines of systemic therapy since late 2019. Real-world outcome data have been limited by lack of comparison to historical care. Using propensity-weighted analysis, we compared 3-year survival, healthcare resource utilization (HRU), and hospitalization events for patients with RR-LBCL treated with CAR-T (n=85) versus historical controls (HC) treated before CAR-T approval who would have been eligible based on present criteria (n=150). CAR-T 3-year overall survival (OS) was 59% ([95% CI, 42-72]; median not-reached) vs 10% ([95% CI, 5-16%]; median 4.4 months) in HC. 3-year CAR-T progression-free survival (PFS) was 48% ([95% CI, 32-63]; median 14.4 months) vs 6% ([95% CI, 3-11]; median 3.6 months) in HC. Hazard ratio (HR) for OS was 0.21 (95% CI, 0.14-0.31), and for PFS was 0.28 (95% CI, 0.2-0.39), comparing CAR-T vs HC. Per 1000 person-days at risk, CAR-T patients had fewer hospital admissions than HC (5.32 vs 9.1), emergency visits (2.33 vs 4.81), and intensive care admissions (0.53 vs 1.25), plus lower hospitalization rates for fever (0.93 vs 1.63), infection (1.48 vs 3.08), and neutropenia (0.66 vs 1.97; all p<0.001). CAR-T produced a sustained survival benefit and, despite well-described CAR-T toxicities, HC experienced more hospitalization events, underscoring the lack of effective salvage treatments. As one of the largest real-world comparisons of RR-LBCL patients receiving CAR-T vs previous standard-of-care, this study demonstrated its improved effectiveness and reduced HRU.
BACKGROUND: The risk of all-cause mortality can inform decision-making for chronic disease prevention. We developed a predictive algorithm to estimate the 5-year risk of death among community-dwelling adults. METHODS: We derived and validated the Mortality Population Risk Tool (MPoRT) using data from population health surveys in Canada (the Canadian Community Health Survey) and the United States (the National Health Interview Survey), survey years 2001 to 2011, linked to vital statistics. The outcome was death within five years of the survey response. The algorithm was developed using data from Ontario respondents using a Cox proportional hazards model, then modified and re-estimated to allow cross-national assessment in Canada and the United States. Twenty-three prespecified predictors were assessed: seven sociodemographic, six behavioural, and ten general health and chronic disease. RESULTS: 527,369 respondents aged 20 to 105 years were included in the Canadian and United States development and validation cohorts, with 43,758 deaths during 3.68 million person-years follow-up. The final sex-specific MPoRT algorithms each contained 21 variables, showing strong discrimination (C-statistic: females 0.874 [0.871--0.877]; males 0.867 [0.865--0.871]) and good calibration overall and in 246 of 247 subgroups. Discrimination was modestly attenuated (0.01 decrease in C-statistic) in cross-national validation between Canada and the United States, with good calibration across all 71 subgroups. INTERPRETATION: MPoRT accurately discriminated all-cause mortality using only self-reported data, enabling broad application without clinical measures. While validation outside North America is needed to confirm broader applicability, MPoRT is designed for straightforward recalibration using routinely available national mortality data. This supports targeted chronic disease prevention strategies at both the population and individual levels, though the limitations inherent to self-reported predictors should be considered when interpreting predictions. ### Competing Interest Statement The authors have declared no competing interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ottawa Health Science Network Research Ethics Board of the Ottawa Health Research Institute waived ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Three sources of data were used for this study: Ontario CCHS (ICES Data): The dataset from this study is held securely in coded form at ICES. While data-sharing agreements prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access (available at www.ices.on.ca/DAS). The full dataset creation plan and underlying analytic code are available from the authors upon reasonable request. United States NHIS Data: The National Health Interview Survey (NHIS) public-use data files are freely available to researchers and the general public without special permissions or restricted access, and can be downloaded directly from the NCHS website (https://www.cdc.gov/nchs/nhis/data-questionnaires-documentation.htm). Restricted-use data files are available through the NCHS Research Data Center (RDC) subject to an application process. National Canadian CCHS Data: The Canadian version of the CCHS linked to mortality is available at Statistics Canada Regional Data Centres (RDCs). Access to these secure microdata files is restricted to affiliated researchers who apply and are approved through Statistics Canada (https://www.statcan.gc.ca/en/microdata/data-centres).
Proton pump inhibitor (PPI) use has been associated with increased prostate cancer risk in case-control and cohort studies of men with a negative prostate biopsy. We estimated the association of cumulative PPI use with prostate cancer diagnosis and outcomes in a biopsy-naïve cohort of men using provincewide-linked administrative data from Ontario, Canada, including men ≥66 years with no prior prostate cancer diagnosis, biopsy, or treatment and no PPI/histamine-2-blocker prescriptions within the year preceding study inclusion (January 2003-December 2018). The primary outcome was time to prostate cancer diagnosis. Associations were evaluated using univariable/multivariable logistic regression models with complementary log-log modeling. Median follow-up was 9.2 years (n = 559,425), and 30.2% had any PPI use during follow-up. Annual PPI use increased from 2.4% (2003) to 13.8% (2019). Cumulative PPI use was associated with lower prostate cancer diagnosis rates [hazard ratio (HR) for highest user quintile vs. nonusers: 0.75, 95% confidence interval (CI), 0.71-0.79]. Following adjustment for the frequency of general practitioner visits and prostate-specific antigen tests in the preceding 2 years, PPI use was no longer associated with prostate cancer diagnosis (HR, 0.99; 95% CI, 0.93-1.05). No association was observed between PPI use and rates of clinically significant or high-grade prostate cancer (HRs, 0.99 and 1.04, respectively). Cumulative PPI use was associated with up to 17% lower rates of a first androgen deprivation therapy prescription or bilateral orchiectomy. Cumulative PPI use was not associated with prostate cancer diagnosis rates, including clinically significant and high-grade prostate cancer. These findings do not support an independent association between PPI use and prostate cancer risk. SIGNIFICANCE:Among biopsy-naïve older men, cumulative PPI use was not associated with prostate cancer diagnosis, including clinically significant and high-grade disease, after adjusting for healthcare resource utilization. These findings challenge prior observational signals suggesting increased risk and indicate that detection bias likely explains earlier associations.
Abstract We examined inequalities in weekly hospitalization rates related to COVID-19, influenza, and respiratory syncytial virus based on neighbourhood-level material deprivation, neighbourhood-level ethno-racial diversity, rurality, and sex. Hospitalization rates were measured as the weekly number of hospitalizations per 10 000 people. Local regression models were used to fit observed weekly hospitalization rates within each study year and each population subgroup, and statistical significance of between-subgroup differences in peak hospitalization rate was assessed based on the overlap of 95% confidence intervals. We also provided comparisons of pre-pandemic (July 2017–June 2020) versus post-pandemic (July 2020–June 2023) trends. More materially deprived neighbourhoods showed up to a 2.5-times increase in peak weekly hospitalization rates compared to less materially deprived neighbourhoods. Peak hospitalization rates were up to 2.3-times higher among more versus less diverse neighbourhoods. Some age groups showed peak hospitalization rates up to 2.8-times higher among urban residents. Peak hospitalization rates were up to 1.3-times higher in younger males versus younger females and up to 1.6-times higher in older females versus older males. Variations due to neighbourhood-level material deprivation, neighbourhood-level ethno-racial diversity, rurality, and sex should be considered when designing and implementing public health policies aiming to reduce respiratory virus burden.
