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Background: Given that pregnant women taking medications are excluded from clinical trials, real-world evidence is essential. We aimed to build a Canadian Mother-Child Cohort Active Surveillance Initiative (CAMCCO) and compare frequency of prematurity, low-birth-weight (LBW), major malformations, multiplicity, and gestational medication use across four provinces., Methods: CAMCCO is a collaborative research infrastructure that uses real-world data from large provincial health care databases in Canada; developed with standardized methods to similarly construct population-based pregnancy/child cohorts with longitudinal follow-up by linking administrative/hospital/birth databases. CAMCCO also includes a common repository to i) share algorithms and case definitions based on diagnostic and procedural codes for research/training purpose, and ii) download aggregate data relevant to primary care providers, researchers, and decision makers. For this study, data from Quebec (1998-2015), Manitoba (1995-2019), Saskatchewan (1996-2020), and Alberta (2005-2018) are compared (Chi-square tests, p-values), and trends are calculated using Cochran-Armitage trend tests., Results: Almost two-thirds (61%) of women took medications during pregnancy, mostly antibiotics (26%), asthma drugs (8%), and antidepressants (4%). Differences in the prevalence of prematurity (5.9-6.8%), LBW (4.0-5.2%), and multiplicity (1.0-2.5%) were statistically significant between provinces (p<0.001). Frequency of major malformations increased over time in Quebec (7-11%; p<0.001), Saskatchewan (5-11%; p<0.001), and Alberta (from 7-8%; p<0.001), and decreased in Manitoba (5-3%; p<0.001). Cardiovascular and musculoskeletal malformations were the most prevalent., Interpretation: Medications are often used among Canadian pregnancies but adverse pregnancy outcomes vary across provinces. Digitized health data may help researchers and care providers understand the risk-benefit ratios related to gestational medication use, as well as province-specific trends., Competing Interests: The authors have declared that no competing interests exist.

Background: Predicting long-term survival after admission to hospital is helpful for clinical, administrative and research purposes. The Hospital-patient One-year Mortality Risk (HOMR) model was derived and internally validated to predict the risk of death within 1 year after admission. We conducted an external validation of the model in a large multicentre study., Methods: We used administrative data for all nonpsychiatric admissions of adult patients to hospitals in the provinces of Ontario (2003-2010) and Alberta (2011-2012), and to the Brigham and Women's Hospital in Boston (2010-2012) to calculate each patient's HOMR score at admission. The HOMR score is based on a set of parameters that captures patient demographics, health burden and severity of acute illness. We determined patient status (alive or dead) 1 year after admission using population-based registries., Results: The 3 validation cohorts (n = 2,862,996 in Ontario, 210 595 in Alberta and 66,683 in Boston) were distinct from each other and from the derivation cohort. The overall risk of death within 1 year after admission was 8.7% (95% confidence interval [CI] 8.7% to 8.8%). The HOMR score was strongly and significantly associated with risk of death in all populations and was highly discriminative, with a C statistic ranging from 0.89 (95% CI 0.87 to 0.91) to 0.92 (95% CI 0.91 to 0.92). Observed and expected outcome risks were similar (median absolute difference in percent dying in 1 yr 0.3%, interquartile range 0.05%-2.5%)., Interpretation: The HOMR score, calculated using routinely collected administrative data, accurately predicted the risk of death among adult patients within 1 year after admission to hospital for nonpsychiatric indications. Similar performance was seen when the score was used in geographically and temporally diverse populations. The HOMR model can be used for risk adjustment in analyses of health administrative data to predict long-term survival among hospital patients., (© 2015 Canadian Medical Association or its licensors.)