Public health utility of cause of death data: applying empirical algorithms to improve data quality

Background: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. Methods: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. Results: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7 of all vital registration deaths to 67.3 in 2015, and the age-standardized proportion had an overall negative association with the Socio-Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30 of garbage-coded deaths in those under 1Â year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD. Conclusions: We provide a detailed description of redistribution methods developed for CoD data in the GBD; these methods represent an overall improvement in empiricism compared to past reliance on a priori knowledge

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Type 2 diabetes after a pregnancy with gestational diabetes among First Nations women in Australia: the PANDORA study.

Aims: To determine among First Nations and Europid pregnant women the cumulative incidence and predictors of postpartum type 2 diabetes and prediabetes and describe post-partum cardiovascular disease (CVD) risk profiles. Methods: PANDORA is a prospective longitudinal cohort of women recruited in pregnancy.Ethnic-specific rates of postpartum type 2 diabetes and prediabetes were reported for women with diabetes in pregnancy (DIP), gestational diabetes (GDM) or normoglycaemia in pregnancy over a short follow-up of 2.5 years (n = 325). Pregnancy characteristics and CVD risk profiles according to glycaemic status, and factors associated with postpartum diabetes/prediabetes were examined in First Nations women. Results: The cumulative incidence of postpartum type 2 diabetes among women with DI Por GDM were higher for First Nations women (48%, 13/27, women with DIP, 13%, 11/82, GDM), compared to Europid women (nil DIP or GDM p < 0.001). Characteristics associated with type 2 diabetes/prediabetes among First Nations women with GDM/DIP included, older age, multiparity, family history of diabetes, higher glucose values, insulin use and body mass index (BMI). Conclusions: First Nations women experience a high incidence of postpartum type 2 diabetes after GDM/DIP, highlighting the need for culturally responsive policies at an individual and systems level, to prevent diabetes and its complications.Anna J. Wood, Jacqueline A. Boyle, Elizabeth L.M. Barr, Federica Barzi, Matthew J.L. Hare, Angela Titmuss, Danielle K. Longmore, Elizabeth Death, Joanna Kelaart, Marie Kirkwood, Sian Graham, Christine Connors, Elizabeth Moore, Kerin O, Dea, Jeremy J.N. Oats, Harold D. McIntyre, Paul Z. Zimmet, Zhong X. Lu, Alex Brown, Jonathan E. Shaw, Louise J. Maple-Brow