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Marc Simard,1– 4 Elham Rahme,5 Marjolaine Dubé,1 Véronique Boiteau,1 Denis Talbot,2,3 Miceline Mésidor,2,3 Yohann Moanahere Chiu,1,4,6 Caroline Sirois1,3,4,6 1Institut national de santé publique du Québec, Québec, QC, Canada; 2Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada; 3Centre de recherche du CHU de Québec, Québec, QC, Canada; 4VITAM-Centre de recherche en santé durable, Québec, QC, Canada; 5Department of Medicine, Division of Clinical Epidemiology, McGill University, and Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, QC, Canada; 6Faculty of de Pharmacy, Université Laval, Québec, QC, CanadaCorrespondence: Marc Simard, Institut national de santé publique du Québec, 945, Wolfe, 5e étage, Québec, QC, G1V 5B3, Canada, Tel +1 418 650 5115 #5522, Fax +1 418 643 5099, Email marc.simard@inspq.qc.caPurpose: To identify multimorbidity trajectories among older adults and to compare their health outcome predictive performance with that of cross-sectional multimorbidity thresholds (eg, ≥ 2 chronic conditions (CCs)).Patients and Methods: We performed a population-based longitudinal study with a random sample of 99,411 individuals aged > 65 years on April 1, 2019. Using health administrative data, we calculated for each individual the yearly CCs number from 2010 to 2019 and constructed the trajectories with latent class growth analysis. We used logistic regression to determine the increase in predictive capacity (c-statistic) of multimorbidity trajectories and traditional cross-sectional indicators (≥ 2, ≥ 3, or ≥ 4 CCs, assessed in April 2019) over that of a baseline model (including age, sex, and deprivation). We predicted 1-year mortality, hospitalization, polypharmacy, and frequent general practitioner, specialist, or emergency department visits.Results: We identified eight multimorbidity trajectories, each representing between 3% and 25% of the population. These trajectories exhibited trends of increasing, stable, or decreasing number of CCs. When predicting mortality, the 95% CI for the increase in the c-statistic for multimorbidity trajectories [0.032– 0.044] overlapped with that of the ≥ 3 indicator [0.037– 0.050]. Similar results were observed when predicting other health outcomes and with other cross-sectional indicators.Conclusion: Multimorbidity trajectories displayed comparable health outcome predictive capacity to those of traditional cross-sectional multimorbidity indicators. Given its ease of calculation, continued use of traditional multimorbidity thresholds remains relevant for population-based multimorbidity surveillance and clinical practice.Keywords: multimorbidity, trajectories, prevalence, health outcome prediction, population-based

Nakhla M, Simard M, Dube M, et al. Clin Epidemiol. 2019;11:833–843. The authors have advised there is an error in the diagnostic codes used to validate the cases of diabetes. The code “251.X” was never included in the validation algorithm and was erroneously included in the list of ICD-9 codes in the final revision phase of the manuscript. The authors apologize for this error. Page 835, Diagnostic accuracy section, second sentence, the text “We determined the diagnostic accuracy (sensitivity, specificity, PPV, NPV) of a variety of algorithms, using combinations of physician billings and hospital admissions over 1 or 2 years bearing a diagnosis code of diabetes mellitus (ICD-9 250.X, 251.X; ICD-10 E10.X-14. X)” should read “We determined the diagnostic accuracy (sensitivity, specificity, PPV, NPV) of a variety of algorithms, using combinations of physician billings and hospital admissions over 1 or 2 years bearing a diagnosis code of diabetes mellitus (ICD-9 250.X; ICD-10 E10.X-14. X)”. Read the original article

Meranda Nakhla,1,2 Marc Simard,3 Marjolaine Dube,3 Isabelle Larocque,3 Céline Plante,3 Laurent Legault,1,2 Celine Huot,4 Nancy Gagné,5 Julie Gagné,6 Sarah Wafa,2 Eric I Benchimol,7–9 Elham Rahme10 1Department of Pediatrics, Division of Endocrinology, Montreal Children’s Hospital, Montreal, QC, Canada; 2Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; 3Institut National de Santé Publique du Québec, Québec, QC, Canada; 4Department of Pediatrics, Division of Endocrinology, Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada; 5Department of Pediatrics, Division of Endocrinology, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada; 6Department of Pediatrics, Division of Endocrinology, Centre Hospitalier de l’Université Laval, Quebec City, QC, Canada; 7Children’s Hospital of Eastern Ontario IBD Centre, Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, Ottawa, Canada; 8Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada; 9Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; 10Department of Medicine, Division of Clinical Epidemiology, McGill University, Montreal, QC, CanadaCorrespondence: Meranda NakhlaCenter of Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, 5252 de Maisonneuve Blvd, W, 3rd floor, office E3.08, Montreal H4A 3S5, QC, CanadaEmail meranda.nakhla@mcgill.caBackground: Type 1 diabetes is one of the most common chronic diseases in childhood with a worldwide incidence that is increasing by 3–5% per year. The incidence of type 2 diabetes, traditionally viewed as an adult disease, is increasing at alarming rates in children, paralleling the rise in childhood obesity. As the rates of diabetes increase in children, accurate population-based assessment of disease burden is important for those implementing strategies for health services delivery. Health administrative data are a powerful tool that can be used to track disease burden, health services use, and health outcomes. Case validation is essential in ensuring accurate disease identification using administrative databases.Aim: The aim of our study was to define and validate a pediatric diabetes case ascertainment algorithm (including any form of childhood-onset diabetes) using health administrative data.Research design and methods: We conducted a two-stage method using linked health administrative data and data extracted from charts. In stage 1, we linked chart data from a large urban region to health administrative data and compared the diagnostic accuracy of various algorithms. We selected those that performed the best to be validated in stage 2. In stage 2, the most accurate algorithms were validated with chart data within two other geographic areas in the province of Quebec.Results: Accurate identification of diabetes in children (ages ≤15 years) required four physician claims or one hospitalization (with International Classification of Disease codes within 1 year (sensitivity 91.2%, 95% confidence interval [CI] 89.2–92.9]; positive predictive value [PPV] 93.5%, 95% CI 91.7–95.0) or using only four physician claims in 2 years (sensitivity 90.4%, 95% CI 88.3–92.2; PPV 93.2%, 95% CI 91.7–95.0). Separating the physician claims by 30 days increased the PPV of all algorithms tested.Conclusion: Patients with child-onset diabetes can be accurately identified within health administrative databases providing a valid source of information for health care resource planning and evaluation.Keywords: pediatric, validation, health administrative data, diabetes

Romina Pace,1 Elham Rahme,1,2 Deborah Da Costa,1,2 Kaberi Dasgupta1,2 1Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; 2Department of Medicine, McGill University Health Center, Montreal, QC, Canada Purpose: The aim of this study was to examine the association between gestational diabetes mellitus (GDM) and depression incidence in mothers and fathers during prenatal and postnatal periods.Patients and methods: Matched pairs (GDM vs no GDM) of randomly selected mothers with singleton live births (matched by age group, delivery year, and health region) and their partners (Quebec, Canada; cohort inception 1990–2007) were assessed for a composite outcome of depression/self-harm/suicide using a health administrative database. We examined the association of GDM and the composite outcome in the following three nonoverlapping periods: 1) 24 weeks gestation up to delivery; 2) delivery up to 1 year postpartum; and 3) 1 year postpartum to study end (March 31, 2012). We used stratified Cox proportional regression hazards models, with three models in mothers and three models in fathers, corresponding to each of the time periods of interest.Results: In the 58,400 mothers, women with GDM had a nearly twofold greater risk (adjusted HR: 1.82, 95% CI 1.28, 2.59) of being diagnosed with depression compared to those without GDM during the prenatal period. In the first year postpartum, there was no conclusive difference observed between the two groups of mothers (adjusted HR: 1.05, 95% CI 0.84, 1.30). Beyond the first year postpartum, there was an 8% increased risk (adjusted HR: 1.08, 95% CI 1.03, 1.14) of depression in women with a history GDM compared to those without. A total of 63,384 fathers were included in our analyses, and no association between GDM in one’s partner and depression was found during any of the three time periods evaluated.Conclusion: GDM is associated with an increased risk of depression in women particularly during pregnancy highlighting the need to screen for depression and provide supportive interventions during this period. Keywords: depression, self-harm, suicide, administrative health data, pregnancy, postpartum

Objective: To assess the risk of hospitalized infection among initiators of disease-modifying anti-rheumatic drugs (DMARDs) and/or anti-tumour necrosis factor (anti-TNF) agents in ankylosing spondylitis (AS).Method: We studied AS patients, new users of anti-TNF drugs and/or DMARDs between 1 January 2001 and 31 December 2011. Cohort entry was defined as the date of first prescription of any of these drugs. We used Cox regression with three time-varying drug exposures: current use of DMARDs without biologics, current use of anti-TNF agents alone or in combination with DMARDs (anti-TNF ± DMARDs), and current non-use. Models were adjusted for baseline patient sociodemographic characteristics, comorbidity, outpatient visits and procedures, previous infection, non-steroidal anti-inflammatory drugs, and corticosteroids. Hospitalized infection was defined on the basis of hospitalization discharge diagnoses (primary or non-primary) coding for infection.Results: The cohort included 747 AS patients, with a mean age of 51.1 years (sd 14.6), and 466 (62.4%) were men. During the median follow-up of 1.98 years, 57 hospitalized infections occurred, for an incidence rate of 2.9/100 person-years. The adjusted hazard ratio of infection (relative to unexposed) was 1.00 [95% confidence interval (CI) 0.47-2.11] for the anti-TNF ± DMARDs group and 0.96 (95% CI 0.45-2.04) for DMARDs alone. Use of healthcare, corticosteroids, and previous hospitalized infections were associated with infection.Conclusion: We found no clear evidence that the risk of hospitalized infection was linked to DMARD and/or anti-TNF drug use. Because of scarce published literature on infection risk in AS patients, our results have important implications for clinicians. [ABSTRACT FROM AUTHOR]