Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

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Julian Little and colleagues present the STREGA recommendations, which are aimed at improving the reporting of genetic association studies.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not JPH -Year 7, Volume 6, Number 3, 2009 F R E E P A P E R S 2 3 9 prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis. I T A L I A N J O U R N A L O F P U B L I C H E A L T H

Cape Breton County contains one of the most polluted areas in North America and is socioeconomically depressed. We evaluated mortality patterns in this area over the past 5 decades, focusing on life expectancy and life loss. Life loss refers to the difference in life expectancy of Cape Breton County residents and all Canadians, and was further broken down into disease-specific components using cause-eliminated life table methods. We observed lags in health of 20 to 25 years for residents of Cape Breton County. Life expectancy in some municipalities of Cape Breton County is reduced by more than 5 years. Life loss for these residents is greater than that of any single cause of death for Canadians. Life loss among Cape Breton County women is primarily attributable to cancer, and, among men, to cardiovascular diseases. Life loss from cancer is higher in the steel-producing communities; whereas life loss from respiratory diseases and lung cancer is higher in the coal mining communities. These (and other) decompositions of life loss disclose patterns in health deficiencies that give rise to etiologic hypotheses and provide clues and directions for prevention and interventions.

The objective of this study was to assess the prognostic validity of Child-Turcotte classification with regard to short-term (1-year) survival. The Child-Turcotte classification, as modified by Pugh et al., was recorded on admission in 177 cirrhotic patients. The variables that comprise the Pugh modification are ascites, encephalopathy, serum albumin, serum bilirubin and prothrombin time. Using multiple logistic regression, we evaluated the contribution of different models to the likelihood of survival, defining different ways to use the Pugh score. The Pugh score categorized in three strata (5 to 6, 7 to 9 and 10 to 15) captured less variance in the survival than the Pugh score counted from 5 to 15. This, in turn, captured less variance than a model in which the parameters of the Pugh score were analyzed according to their original units. The prediction rule based on the last model was tested in another sample of cirrhotics. The "original unit" model was studied in both training and testing samples, using receiver-operating characteristic curves to evaluate its clinical validity (sensitivity and specificity). The prediction rule based on the "original units" Pugh score allowed for a good discrimination of patients who lived and those who died. (At the point of maximum discrimination, sensitivity and specificity reached a mean of 80%). Validity of the prediction rule was confirmed by the testing sample. The qualities of simplicity, availability, low cost and good discriminating power for a life or death outcome make the Pugh score a very useful method to estimate prognosis in patients with cirrhosis.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Summary Infections are suspected to play a role in the aetiology of childhood leukaemia. In 1989-95, we evaluated the relation between childhood acute lymphoblastic leukaemia and pre-and postnatal markers of exposure to infection, as well as breast-feeding. A populationbased case-control study was carried out in certain regions of Québec, Canada, in 1989-95 including 491 incident cases diagnosed between 1980 and 1993 and aged between 0 and 9 years. An identical number of healthy controls matched for age, sex and region of residence at the date of diagnosis was included. Having older siblings, mother's use of antibiotics during pregnancy, and being born second or later were all associated with increased risk of leukaemia while early day-care attendance (odds ratio (OR) = 0.49; 95% CI 0.31-0.77), and breastfeeding (OR = 0.68; 95% CI 0.49-0.95) were significantly protective. A marker of population mixing was not a risk factor. When including all variables defining family structure in a model, having older siblings at time of diagnosis was a risk factor among children diagnosed before 4 years of age (OR = 4.54; 95% CI 2.27-9.07) whereas having older siblings in the first year of life was protective among children diagnosed at 4 years of age or later (OR = 0.46; 95% CI 0.22-0.97).