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Record W3082505470 · doi:10.1101/2020.08.27.20183434

Risk factors for severe outcomes of COVID-19: a rapid review

2020· review· en· W3082505470 on OpenAlexaffabout
Aireen Wingert, Jennifer Pillay, Michelle Gates, Samantha Guitard, Sholeh Rahman, Andrew Beck, Ben Vandermeer, Lisa Hartling

Bibliographic record

VenuemedRxiv · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineConfoundingMEDLINECoronavirus disease 2019 (COVID-19)Data extractionGerontologyFamily medicineDemographyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Background Identification of high-risk groups is needed to inform COVID-19 vaccine prioritization strategies in Canada. A rapid review was conducted to determine the magnitude of association between potential risk factors and risk of severe outcomes of COVID-19. Methods Methods, inclusion criteria, and outcomes were prespecified in a protocol that is publicly available. Ovid MEDLINE(R) ALL, Epistemonikos COVID-19 in L·OVE Platform, and McMaster COVID-19 Evidence Alerts, and select websites were searched to 15 June 2020. Studies needed to be conducted in Organisation for Economic Co-operation and Development countries and have used multivariate analyses to adjust for potential confounders. After piloting, screening, data extraction, and quality appraisal were all performed by a single reviewer. Authors collaborated to synthesize the findings narratively and appraise the certainty of the evidence for each risk factor-outcome association. Results Of 3,740 unique records identified, 34 were included in the review. The studies included median 596 (range 44 to 418,794) participants with a mean age between 42 and 84 years. Half of the studies (17/34) were conducted in the United States and 19/34 (56%) were rated as good quality. There was low or moderate certainty evidence for a large (≥2-fold) association with increased risk of hospitalization in people having confirmed COVID-19, for the following risk factors: obesity class III, heart failure, diabetes, chronic kidney disease, dementia, age over 45 years (vs. younger), male gender, Black race/ethnicity (vs. non-Hispanic white), homelessness, and low income (vs. above average). Age over 60 and 70 years may be associated with large increases in the rate of mechanical ventilation and severe disease, respectively. For mortality, a large association with increased risk may exist for liver disease, Bangladeshi ethnicity (vs. British white), age over 45 years (vs. <45 years), age over 80 years (vs. 65-69 years), and male gender in those 20-64 years (but not older). Associations with hospitalization and mortality may be very large (≥5-fold increased risk) for those aged over 60 years. Conclusion Among other factors, increasing age (especially >60 years) appears to be the most important risk factor for severe outcomes among those with COVID-19. There is a need for high quality primary research (accounting for multiple confounders) to better understand the level of risk that might be associated with immigration or refugee status, religion or belief system, social capital, substance use disorders, pregnancy, Indigenous identity, living with a disability, and differing levels of risk among children. PROSPERO registration CRD42020198001 What is already known The novel nature of COVID-19 means that in many countries there are currently no pre-determined priority groups for the receipt of the eventual COVID-19 vaccine(s). Primary research is rapidly emerging, but consensus on who might be at increased risk of severe outcomes from COVID-19 has not been established. What this study adds This rapid review shows that advancing age (>45 years and especially >60 years) may be the most important risk factor for hospitalization and mortality from COVID-19. Other important risk factors for severe disease identified by this review include several pre-existing chronic conditions (class III obesity, heart failure, diabetes, chronic kidney disease, liver disease, dementia), male gender, Black race/ethnicity (vs. non-Hispanic white), Bangladeshi ethnicity (vs. British white), low income (vs. high), and homelessness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.452
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.452
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.220
GPT teacher head0.513
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2020
Admission routes2
Has abstractyes

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