Evidence map›Paper›PMID 33031532›Full record

ArticleJournal of the National Cancer Institute2021

Prevalence and Outcome of COVID-19 Infection in Cancer Patients: A National Veterans Affairs Study.

Nathanael R Fillmore, Jennifer La, Raphael E Szalat, David P Tuck, Vinh Nguyen, Cenk Yildirim, Nhan V Do, Mary T Brophy, Nikhil C Munshi

Open access · hybridAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed, 5 pooled it
6.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

68 citing papers in PubMed, 5 syntheses or guidelines pooled it, 102 citations in OpenAlex.

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  7. Interferon-α Nasal Spray Prophylaxis Reduces COVID-19 in Cancer Patients: A Randomized, Double-Blinded, Placebo-Controlled Trial.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
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8 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Nathanael R FillmoreCSP Informatics Center, MAVERIC, VA Boston Healthcare System, Boston, MA, USA.ORCID 0000-0002-8058-3423
Jennifer LaCSP Informatics Center, MAVERIC, VA Boston Healthcare System, Boston, MA, USA.
Raphael E SzalatHematology and Oncology Department, VA Boston Healthcare System, Boston, MA, USA.ORCID 0000-0002-6198-3405
David P TuckHematology and Oncology Department, VA Boston Healthcare System, Boston, MA, USA.
Vinh NguyenCSP Informatics Center, MAVERIC, VA Boston Healthcare System, Boston, MA, USA.ORCID 0000-0001-8886-0117
Cenk YildirimCSP Informatics Center, MAVERIC, VA Boston Healthcare System, Boston, MA, USA.
Nhan V DoCSP Informatics Center, MAVERIC, VA Boston Healthcare System, Boston, MA, USA.
Mary T BrophyCSP Informatics Center, MAVERIC, VA Boston Healthcare System, Boston, MA, USA.
Nikhil C MunshiHematology and Oncology Department, VA Boston Healthcare System, Boston, MA, USA.
VA Boston Healthcare System · USBoston University · US

Funding

Trageting the Multiple Myeloma EpigenomeP50CA100707 · NCI · DANA-FARBER CANCER INSTITUTE · PI ANDERSON, KENNETH C., MUNSHI, NIKHIL C. · 2003 to 2023
$45.0M
Targeting Genomic Instability and Evolution in MyelomaP01CA155258 · NCI · DANA-FARBER CANCER INST · PI RICHARD YOUNG · 2011 to 2026
$32.5M
MOLECULAR MANIPULATION TO ENHANCE ANTI-MYELOMA RESPONSEI01BX001584 · VA · VA BOSTON HEALTH CARE SYSTEM · PI MUNSHI, NIKHIL C. · 2013 to 2025
–
BLRD VA I01 BX001584NCI NIH HHS P01 CA155258NCI NIH HHS P50 CA100707
6 · The paper itself

Abstract

backgroundEmerging data suggest variability in susceptibility and outcome to coronavirus disease 2019 (COVID-19) infection. Identifying risk factors associated with infection and outcomes in cancer patients is necessary to develop healthcare recommendations.

methodsWe analyzed electronic health records of the US Veterans Affairs Healthcare System and assessed the prevalence of COVID-19 infection in cancer patients. We evaluated the proportion of cancer patients tested for COVID-19 who were positive, as well as outcome attributable to COVID-19, and stratified by clinical characteristics including demographics, comorbidities, cancer treatment, and cancer type. All statistical tests are 2-sided.

resultsOf 22 914 cancer patients tested for COVID-19, 1794 (7.8%) were positive. The prevalence of COVID-19 was similar across age. Higher prevalence was observed in African American (15.0%) compared with White (5.5%; P < .001) and in patients with hematologic malignancy compared with those with solid tumors (10.9% vs 7.8%; P < .001). Conversely, prevalence was lower in current smokers and patients who recently received cancer therapy (<6 months). The COVID-19-attributable mortality was 10.9%. Higher attributable mortality rates were observed in older patients, those with higher Charlson comorbidity score, and in certain cancer types. Recent (<6 months) or past treatment did not influence attributable mortality. Importantly, African American patients had 3.5-fold higher COVID-19-attributable hospitalization; however, they had similar attributable mortality as White patients.

conclusionPreexistence of cancer affects both susceptibility to COVID-19 infection and eventual outcome. The overall COVID-19-attributable mortality in cancer patients is affected by age, comorbidity, and specific cancer types; however, race or recent treatment including immunotherapy do not impact outcome.

Indexed as

COVID-19HumansNeoplasmsPrevalenceRisk FactorsUnited StatesUnited States Department of Veterans Affairs

Identifiers

PMID33031532
PMCPMC7665587
OpenAlexW3092001409

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.