Preclinical Target Validation Using Patient-derived Cells.

The Structural Genomics Consortium (SGC) and its clinical, industry and disease-foundation partners are launching open-source preclinical translational medicine studies.

Survival Extrapolation In The Presence Of Cause Specific Hazards.

Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow-up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short-term data, assuming a constant additive or multiplicative effect on the hazards for all-cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause-specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all-cause mortality as a sum of latent cause-specific hazards. Assuming proportional cause-specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause-specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause-specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators.

Brazilian multicentre study of common mental disorders in primary care: rates and related social and demographic factors

Mental health problems are common in primary health care, particularly anxiety and depression. This study aims to estimate the prevalence of common mental disorders and their associations with socio-demographic characteristics in primary care in Brazil (Family Health Strategy). It involved a multicenter cross-sectional study with patients from Rio de Janeiro, São Paulo, Fortaleza (Ceará State) and Porto Alegre (Rio Grande do Sul State), assessed using the General Health Questionnaire (GHQ-12) and the Hospital Anxiety and Depression Scale (HAD). The rate of mental disorders in patients from Rio de Janeiro, São Paulo, Fortaleza and Porto Alegre were found to be, respectively, 51.9%, 53.3%, 64.3% and 57.7% with significant differences between Porto Alegre and Fortaleza compared to Rio de Janeiro after adjusting for confounders. Prevalence proportions of mental problems were especially common for females, the unemployed, those with less education and those with lower incomes. In the context of the Brazilian government's moves towards developing primary health care and reorganizing mental health policies it is relevant to consider common mental disorders as a priority alongside other chronic health conditions.