Objectives: To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems. Research Design: Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values. Results: Combined index [c-statistics: 0.853 (95% confidence interval: CI, 0.848–0.856)] performed better than original Charlson [0.841 (95% CI, 0.835–0.844)] or Elixhauser [0.841 (95% CI, 0.837–0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven: 0.852, Schneeweiss: 0.851, Charlson: 0.849). Results were consistent across both coding systems. Conclusions: The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.

Introduction With the growing burden of chronic diseases, surveillance will play an essential role in improving their prevention and control. The Institut national de santé publique du Québec has developed an innovative chronic disease surveillance system, the Quebec Integrated Chronic Disease Surveillance System (QICDSS). We discuss the primary features, strengths and limitations of this system in this report. Methodology The QICDSS was created by linking five health administrative databases. Updated annually, it currently covers the period from January 1, 1996, to March 31, 2012. The operational model comprises three steps: (1) extraction and linkage of health administrative data according to specific selection criteria; (2) analysis (validation of case definitions essentially) and production of surveillance measures; and (3) data interpretation, submission and dissemination of information. The QICDSS allows the surveillance of the following chronic diseases: diabetes, cardiovascular diseases, respiratory diseases, osteoporosis, osteoarticular diseases, mental disorders, Alzheimer's disease and related disorders. The system also lends itself to the analysis of multimorbidity and polypharmacy. Results For 2011–2012, the QICDSS contained information on 7 995 963 Quebecers with an average age of 40.8 years. Of these, 95.3% met at least one selection criterion allowing the application of case definitions for chronic disease surveillance. The actual proportion varied with age, from 90.1% for those aged 19 years or less to 99.3% for those aged 65 years or over. Conclusion The QICDSS provides a way of producing population-based data on the chronic disease burden, health services and prescription drug uses. The system facilitates the integrated study of several diseases in combination, an approach rarely implemented until now in the context of population surveillance. The QICDSS possesses all the essential features of a surveillance system and supports the dissemination of information to public health decision-makers for future actions.

The robust Poisson method is becoming increasingly popular when estimating the association of exposures with a binary outcome. Unlike the logistic regression model, the robust Poisson method yields results that can be interpreted as risk or prevalence ratios. In addition, it does not suffer from frequent nonconvergence problems such as the most common implementations of maximum likelihood estimators of the log-binomial model. However, using a Poisson distribution to model a binary outcome may seem counterintuitive. Methodologic papers have often presented this as a good approximation to the more natural binomial distribution. In this article, we provide an alternative perspective to the robust Poisson method based on the semiparametric theory. This perspective highlights that the robust Poisson method does not require assuming a Poisson distribution for the outcome. In fact, the method only assumes a log-linear relation between the risk or prevalence of the outcome and the explanatory variables. This assumption and the consequences of its violation are discussed. We also provide suggestions to reduce the risk of violating the modeling assumption. Additionally, we discuss and contrast the robust Poisson method with other approaches for estimating exposure risk or prevalence ratios. See video abstract at, http://links.lww.com/EDE/B987.

Objective: To verify the efficacy of school-based interventions aimed at reducing sugar-sweetened beverage (SSB) consumption among adolescents in order to develop or improve public health interventions.Design: Systematic review of interventions targeting adolescents and/or the school environment.Setting: The following databases were investigated: MEDLINE/PubMed, PsycINFO, CINAHL and EMBASE. Proquest Dissertations and Theses was also investigated for unpublished trials.Subjects: Adolescents were defined as individuals between the ages of 12 and 17 years.Results: A total of thirty-six studies detailing thirty-six different interventions tested among independent samples (n152 001) were included in the review. Twenty interventions were classified as educational/behavioural and ten were classified as legislative/environmental interventions. Only six interventions targeted both individuals and their environment. Over 70 % of all interventions, regardless of whether they targeted individuals, their environment or both, were effective in decreasing SSB consumption. Legislative/environmental studies had the highest success rate (90·0 %). Educational/behavioural interventions only and interventions that combined educational/behavioural and legislative/environmental approaches were almost equally effective in reducing SSB consumption with success rates of 65·0 and 66·7 %, respectively. Among the interventions that had an educational/behavioural component, 61·5 % were theory-based. The behaviour change techniques most frequently used in interventions were providing information about the health consequences of performing the behaviour (72·2 %), restructuring the physical environment (47·2 %), behavioural goal setting (36·1 %), self-monitoring of behaviour (33·3 %), threat to health (30·6 %) and providing general social support (30·6 %).Conclusions: School-based interventions show promising results to reduce SSB consumption among adolescents. A number of recommendations are made to improve future studies.

The effects of gender, time variables, menstrual cycle phases, plasma sex hormone concentrations and physiologic urinary pH on CYP2D6 phenotyping were studied using two widely employed CYP2D6 probe drugs, namely dextromethorphan and metoprolol. Phenotyping on a single occasion of 150 young, healthy, drug-free women and men revealed that the dextromethorphan: dextrorphan metabolic ratio (MR) was significantly lower (P < 0.0001) in 56 female extensive metabolizers (0.008+/-0.021) compared to 86 male extensive metabolizers (0.020 +/-0.040). Urinary pH was a significant predictor of dextromethorphan: dextrorphan MRs in men and women (P < 0.001). Once-a-month phenotyping with dextromethorphan of 12 healthy young men (eight extensive metabolizers and four poor metabolizers) over a 1-year period, as well as every-other-day phenotyping with dextromethorphan of healthy, pre-menopausal women (10 extensive metabolizers and 2 poor metabolizers) during a complete menstrual cycle, did not follow a particular pattern and showed similar intrasubject variability ranging from 24.1% to 74.5% (mean 50.9%) in men and from 20.5% to 96.2% (mean 52.0%) in women, independent of the CYP2D6 phenotype (P = 0.342). Using metoprolol as a probe drug, considerable intrasubject variability (38.6+/- 12.0%) but no correlation between metoprolol: alpha-hydroxymetoprolol MRs and pre-ovulatory, ovulatory and luteal phases (mean +/- SD metoprolol: a-hydroxymetoprolol MRs: 1.086+/- 1.137 pre-ovulatory; 1.159+/-1.158 ovulatory and 1.002+/-1.405 luteal phase; P> 0.9) or 17beta-oestradiol, progesterone or testosterone plasma concentrations was observed. There was a significant inverse relationship between physiologic urinary pH and sequential dextromethorphan: dextrorphan MRs as well as metoprolol: alpha-hydroxymetoprolol MRs in men and women, with metabolic ratios varying up to six-fold with metoprolol and up to 20-fold with dextromethorphan (ANCOVA P < 0.001). We conclude that apparent CYP2D6 activity is highly variable, independent of menstrual cycle phases, sex hormones, time variables or phenotype. Up to 80% of the observed variability can be explained by variations of urinary pH within the physiological range. An apparent phenotype shift as a result of variations in urinary pH may be observed in individuals who have metabolic ratios close to the population antimode.