Quality in Statistical Systems: The Challenge for Puerto Rico

In Puerto Rico, confidence in public data has been deeply compromised. Examination of extant island data demonstrates shortcomings in measuring basic variables that are now crucial in the international context. Demands from researchers, and from governmental and private constituents resulted in Law 209 of August 28, 2003 (The Law).  The Law created the Statistics Institute of Puerto Rico (The Institute) to induce significant changes in statistical production processes and to coordinate the creation of a reliable statistical system for public data on the island.  As part of its mandate, The Institute is implementing a quality assurance process that aims to guarantee rights of all constituents to opportune and reliable information.  This article summarizes and interprets ongoing initiatives taken by The Institute to meet quality assurance objectives of The Law. It also intends to contribute to broader international conversations on statistical quality and its central role in regaining people’s trust in decision making processes.

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