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).
The U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a national user facility, serves the diverse scientific community by providing integrated high-throughput sequencing and ...computational analysis to enable system-based scientific approaches in support of DOE missions related to clean energy generation and environmental characterization. The JGI Genome Portal (http://genome.jgi.doe.gov) provides unified access to all JGI genomic databases and analytical tools. The JGI maintains extensive data management systems and specialized analytical capabilities to manage and interpret complex genomic data. A user can search, download and explore multiple data sets available for all DOE JGI sequencing projects including their status, assemblies and annotations of sequenced genomes. Here we describe major updates of the Genome Portal in the past 2 years with a specific emphasis on efficient handling of the rapidly growing amount of diverse genomic data accumulated in JGI.
Abstract
Background
We compared outcomes in inpatients and outpatients, pre-COVID-19, who were infected with either coronavirus or influenza.
Methods
Using deidentified electronic health records data ...from the Geisinger-Regeneron partnership, we compared patients with RT-PCR–positive tests for the 4 common coronaviruses (229E, HKU1, NL63, OC43) or influenza (A and B) from June 2016 to February 2019.
Results
Overall, 52 833 patients were tested for coronaviruses and influenza. For patients ≥21 years old, 1555 and 3991 patient encounters had confirmed positive coronavirus and influenza tests, respectively. Both groups had similar intensive care unit (ICU) admission rates (7.2% vs 6.1%, P = .12), although patients with coronavirus had significantly more pneumonia (15% vs 7.4%, P < .001) and higher death rate within 30 days (4.9% vs 3.0%, P < .001). After controlling for other covariates, coronavirus infection still had a higher risk of death and pneumonia than influenza (odds ratio, 1.64 and 2.05, P < .001), with no significant difference in ICU admission rates.
Conclusions
Common coronaviruses cause significant morbidity, with potentially worse outcomes than influenza. Identifying a subset of patients who are more susceptible to poor outcomes from common coronavirus infections may help plan clinical interventions in patients with suspected infections.
While common coronavirus infections are thought to be generally mild compared to influenza, this study showed increased risk of both death within 30 days of diagnosis and pneumonia for patients with RT-PCR–confirmed infections.
To develop a dataset based on open data sources reflective of community-level social determinants of health (SDH).
We created FACETS (Factors Affecting Communities and Enabling Targeted Services), an ...architecture that incorporates open data related to SDH into a single dataset mapped at the census-tract level for New York City.
FACETS (https://github.com/mcantor2/FACETS) can be easily used to map individual addresses to their census-tract-level SDH. This dataset facilitates analysis across different determinants that are often not easily accessible.
Wider access to open data from government agencies at the local, state, and national level would facilitate the aggregation and analysis of community-level determinants. Timeliness of updates to federal non-census data sources may limit their usefulness.
FACETS is an important first step in standardizing and compiling SDH-related data in an open architecture that can give context to a patient's condition and enable better decision-making when developing a plan of care.
I am a Jewish chaplain and I felt moved to write this letter to my esteemed colleague Imam Sohaib Sultan, of blessed memory. Sohaib and I each experienced our first unit of Clinical Pastoral ...Education together as part of the same cohort in the summer of 2008. Sohaib died tragically in 2021. Here, I reflect on how we might respond to the current Israel-Hamas War.
Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes
. 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
, 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.
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
) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10
), 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.
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