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ObjectivesTo characterize the experiences of providers in completing the cause of death section on death certificates, with particular reference to deaths in people who have cancer.MethodsFocus groups were conducted until thematic saturation was reached, resulting in four groups over three months. Participants were from a variety of specialties and levels and types of training. Focus groups were recorded and transcribed verbatim and analyzed using constant comparison analysis.ResultsThree types of challenges to case classification were identified. 1) Infrastructural and procedural challenges encountered when completing death certificates, including the rigid structure of the form, lack of training in its completion, and lack of real-time feedback. 2) Clinical uncertainty and the varied approaches providers take to determine the cause of death based on their perception of the purpose of the death certificate. 3) Choosing cause of death in decedents with a history of cancer.ConclusionsThere are specific and substantial challenges in the death certification process that lead to errors in documenting the cause of death, but many of these challenges could be addressed with structural change to the forms or mechanism of training. Using these data to inform change could improve the death certification process and reliability of this data.

Substantial advances have been made in identifying common genetic variants influencing cardiometabolic traits and disease outcomes through genome wide association studies. Nevertheless, gaps in knowledge remain and new questions have arisen regarding the population relevance, mechanisms, and applications for healthcare. Using a new high-resolution custom single nucleotide polymorphism (SNP) array (Metabochip) incorporating dense coverage of genomic regions linked to cardiometabolic disease, the University College-London School-Edinburgh-Bristol (UCLEB) consortium of highly-phenotyped population-based prospective studies, aims to: (1) fine map functionally relevant SNPs; (2) precisely estimate individual absolute and population attributable risks based on individual SNPs and their combination; (3) investigate mechanisms leading to altered risk factor profiles and CVD events; and (4) use Mendelian randomisation to undertake studies of the causal role in CVD of a range of cardiovascular biomarkers to inform public health policy and help develop new preventative therapies.