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The aim of this study was to identify a best practice method to cost the health benefits of active transport for use in infrastructure planning in New South Wales, Australia. We systematically reviewed the international literature covering the concept areas of active transport and cost and health benefits. Original publications describing a method to cost the health benefits of active transport, published in 2000–2019 were included. Studies meeting the inclusion criteria were assessed against criteria identified in interviews with key government stakeholders. A total of 2993 studies were identified, 53 were assessed for eligibility, and 19 were included in the review. The most commonly studied active transport modes were cycling (n = 8) and walking and cycling (n = 6). Exposures considered were physical activity, road transport related injuries and air pollution. The most often applied economic evaluation method was cost benefit analysis (n = 8), and costs were commonly calculated by monetising health outcomes. Based on evaluation of models against the criteria, a Multistate Life Table model was recommended as the best method currently available. There is strong and increasing interest in quantifying and costing the health benefits of active transport internationally. Incorporating health-related economic benefits into existing regulatory processes such as cost benefit analyses could provide an effective way to encourage the non-health sector to include health impacts in infrastructure measures.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field. ; A trio of genome-wide association studies recently reported sequence variants at three loci to be significantly associated with schizophrenia. No sequence polymorphism had been unequivocally (P<5 × 10(-8)) associated with schizophrenia earlier. However, one variant, rs1344706[T], had come very close. This polymorphism, located in an intron of ZNF804A, was reported to associate with schizophrenia with a P-value of 1.6 × 10(-7), and with psychosis (schizophrenia plus bipolar disorder) with a P-value of 1.0 × 10(-8). In this study, using 5164 schizophrenia cases and 20,709 controls, we replicated the association with schizophrenia (odds ratio OR = 1.08, P = 0.0029) and, by adding bipolar disorder patients, we also confirmed the association with psychosis (added N = 609, OR = 1.09, P = 0.00065). Furthermore, as it has been proposed that variants such as rs1344706[T]-common and with low relative risk-may also serve to identify regions harboring less common, higher-risk susceptibility alleles, we searched ZNF804A for large copy number variants (CNVs) in 4235 psychosis patients, 1173 patients with other psychiatric disorders and 39,481 controls. We identified two CNVs including at least part of ZNF804A in psychosis patients and no ZNF804A CNVs in controls (P = 0.013 for association with psychosis). In addition, we found a ZNF804A CNV in an anxiety patient (P = 0.0016 for association with the larger set of psychiatric disorders). ; European Union LSHM-CT-2006-037761 PIAP-GA-2008-218251 HEALTH-F2-2009-223423 National Genomic Network (NGFN-2) of the German Federal Ministry of Education and Research (BMBF) National Institute of Mental Health R01 MH078075 Center of Excellence for Complex Disease Genetics of the Academy of Finland 213506 129680 Biocentrum Helsinki Foundation and Faculty of Medicine, University of Helsink info:eu-repo/grantAgreement/EC/FP7/218251