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As population health becomes more of a focus of health care, providers are realizing that data outside of traditional clinical findings can provide a broader perspective on potential drivers of a patient's health status and can identify approaches to improving the effectiveness of care. However, many challenges remain before data related to the social determinants of health, such as environmental conditions and education levels, are as readily accessible and actionable as medical data are. Key challenges are a lack of consensus on standards for capturing or representing social determinants of health in electronic health records and insufficient evidence that once information on them has been collected, social determinants can be effectively addressed through referrals or other action tools. To address these challenges and effectively use social determinants in health care settings, we recommend creating national standards for representing data related to social determinants of health in electronic health records, incentivizing the collection of the data through financial or quality measures, and expanding the body of research that measures the impact of acting on the information collected.

Athough social determinants are increasingly recognized as important influences in health outcomes, obtaining accurate data about social determinants of health (SDH) is often a challenge. Publicly available data may be too coarse to be applicable to individual patients, and screening programs to collect individual-level determinants, although expanding, are not yet optimized. In addition, the debate about the usefulness of community-level versus self-reported SDH data remains open, with the former seen as more useful for policymakers or population health and the latter seen as more useful at the individual clinical encounter level. In this issue of AJPH, Udalova et al. (p. 923) demonstrate an approach to fortifying the utility of community-level data by linking Census Bureau data to clinical data at the individual level. The authors accomplished this through a process that began with a complicated data use agreement that required education, negotiation, and specification on both sides and ended with greater than 94% linkage between protected identification keys from the Census Bureau and the patients in the clinical data set. Although 90% ofthe patient matching was based on Social Security numbers, the algorithm performed nearly as well when Social Security number information was removed from the data set. The authors note that microlevel Census Bureau data may be useful for health systems in determining whether they are serving a representative population, correcting for bias in data sources, and evaluating estimates based on larger area statistics (e.g., Census blocks).

Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes 1 , 2 , 3 , 4 – 5 . Here we use exome sequence data on 628,388 individuals to identify 40,208 carriers of clonal haematopoiesis of indeterminate potential (CHIP). Using genome-wide and exome-wide association analyses, we identify 24 loci (21 of which are novel) where germline genetic variation influences predisposition to CHIP, including missense variants in the lymphocytic antigen coding gene LY75 , which are associated with reduced incidence of CHIP. We also identify novel rare variant associations with clonal haematopoiesis and telomere length. Analysis of 5,041 health traits from the UK Biobank (UKB) found relationships between CHIP and severe COVID-19 outcomes, cardiovascular disease, haematologic traits, malignancy, smoking, obesity, infection and all-cause mortality. Longitudinal and Mendelian randomization analyses revealed that CHIP is associated with solid cancers, including non-melanoma skin cancer and lung cancer, and that CHIP linked to DNMT3A is associated with the subsequent development of myeloid but not lymphoid leukaemias. Additionally, contrary to previous findings from the initial 50,000 UKB exomes 6 , our results in the full sample do not support a role for IL-6 inhibition in reducing the risk of cardiovascular disease among CHIP carriers. Our findings demonstrate that CHIP represents a complex set of heterogeneous phenotypes with shared and unique germline genetic causes and varied clinical implications. Exome sequence data from 628,388 individuals was used to identify 24 risk loci in 40,208 carriers of clonal haematopoiesis of indeterminate potential and link them to other conditions including COVID-19, cardiovascular disease and cancer.

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world 1 . Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community. Exome sequences from the first 49,960 participants in the UK Biobank highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2–2%) that downregulates ACE2 expression by 37% ( P  = 2.7 × 10 − 8 ) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P  = 4.5 × 10 − 13 ), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1 , MHC, DPP9 and IFNAR2 ). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone. Genome-wide meta-analysis of SARS-CoV-2 susceptibility and severity phenotypes in up to 756,646 samples identifies a rare protective variant proximal to ACE2 . A 6-SNP genetic risk score provides additional predictive power when added to known risk factors.

Coronavirus disease 2019 (COVID-19) and influenza are respiratory illnesses caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses, respectively. Both diseases share symptoms and clinical risk factors 1 , but the extent to which these conditions have a common genetic etiology is unknown. This is partly because host genetic risk factors are well characterized for COVID-19 but not for influenza, with the largest published genome-wide association studies for these conditions including >2 million individuals 2 and about 1,000 individuals 3 – 6 , respectively. Shared genetic risk factors could point to targets to prevent or treat both infections. Through a genetic study of 18,334 cases with a positive test for influenza and 276,295 controls, we show that published COVID-19 risk variants are not associated with influenza. Furthermore, we discovered and replicated an association between influenza infection and noncoding variants in B3GALT5 and ST6GAL1 , neither of which was associated with COVID-19. In vitro small interfering RNA knockdown of ST6GAL1—an enzyme that adds sialic acid to the cell surface, which is used for viral entry—reduced influenza infectivity by 57%. These results mirror the observation that variants that downregulate ACE2 , the SARS-CoV-2 receptor, protect against COVID-19 (ref. 7 ). Collectively, these findings highlight downregulation of key cell surface receptors used for viral entry as treatment opportunities to prevent COVID-19 and influenza. Genome-wide analyses identify variants in B3GALT5 and ST6GAL1 associated with influenza susceptibility. Knockdown of ST6GAL1 in cell culture reduces influenza infectivity, likely by interfering with the glycoprotein modifications required for viral entry.

While many aspects of patient care have transitioned to digital technology, the patient registration process often is still paper based. Several studies have examined the effects of changes in clinic workflows and appointment scheduling on patient satisfaction, but few have investigated changes from a paper-based to a paperless registration process. The authors measured patient and staff satisfaction before and after implementation of a new, tablet-based registration process at NYU Langone Health's Center for Women's Health in New York City. Mean preimplementation patient satisfaction scores on the six questions related to the registration process (1-5 scale, with 5 being the highest score) ranged from 4.0 to 4.5. Postimplementation satisfaction scores on the nine questions (six premeasure questions and three additional questions related to the tablet-based process) ranged from 4.4 to 4.6, with four of the six premeasures showing statistically significant improvement in patient satisfaction. Staff satisfaction was generally lower (2.8-3.6 preimplementation and 2.8-4 postimplementation), with no statistically significant difference between time frames. Patient satisfaction was relatively high under the paper registration process, and it improved significantly in some respects under the paperless process, while staff satisfaction did not change. The convenience and ease of use of a paperless registration system can help maintain or increase patient and staff satisfaction while introducing new workflows and improving the efficiency of the outpatient registration process. In adopting technology that can lead to changing workflows, organizations should train staff members and support them during the process.