Evidence map›Paper›PMID 36201388›Full record

ArticleJMIR cancer2022

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study.

Nimish Valvi, Hetvee Patel, Giorgos Bakoyannis, David A Haggstrom, Sanjay Mohanty, Brian E Dixon

Open access · goldAbstract read
In one paragraph

Article in JMIR cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

6 authors at 3 institutions in 1 country.

Nimish Valvi *Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0003-1699-7549
Hetvee PatelRichard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0002-7214-5087
Giorgos BakoyannisRichard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0002-2789-2497
David A HaggstromCenter for Health Information and Communication, Health Services Research & Development Service, Richard L. Roudebush VA Medical Center, Veterans Health Administration, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0003-3637-6950
Sanjay MohantyDepartment of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0003-0295-3457
Brian E Dixon *Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.ORCID https://orcid.org/0000-0002-1121-0607
Regenstrief Institute · USIndiana University – Purdue University Indianapolis · USIndiana University School of Medicine

Funding

Using Routine Care Electronic Medical Record Data and Artificial Intelligence to Develop a Passive Digital Marker to Predict Postoperative DeliriumK23AG071945 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI Sanjay Mohanty · 2022 to 2026
$687k
NIA NIH HHS K23 AG071945
6 · The paper itself

Abstract

backgroundPrior studies, generally conducted at single centers with small sample sizes, found that individuals with cancer experience more severe outcomes due to COVID-19, caused by SARS-CoV-2 infection. Although early examinations revealed greater risk of severe outcomes for patients with cancer, the magnitude of the increased risk remains unclear. Furthermore, prior studies were not typically performed using population-level data, especially those in the United States. Given robust prevention measures (eg, vaccines) are available for populations, examining the increased risk of patients with cancer due to SARS-CoV-2 infection using robust population-level analyses of electronic medical records is warranted.

objectiveThe aim of this paper is to evaluate the association between SARS-CoV-2 infection and all-cause mortality among recently diagnosed adults with cancer.

methodsWe conducted a retrospective cohort study of newly diagnosed adults with cancer between January 1, 2019, and December 31, 2020, using electronic health records linked to a statewide SARS-CoV-2 testing database. The primary outcome was all-cause mortality. We used the Kaplan-Meier estimator to estimate survival during the COVID-19 period (January 15, 2020, to December 31, 2020). We further modeled SARS-CoV-2 infection as a time-dependent exposure (immortal time bias) in a multivariable Cox proportional hazards model adjusting for clinical and demographic variables to estimate the hazard ratios (HRs) among newly diagnosed adults with cancer. Sensitivity analyses were conducted using the above methods among individuals with cancer-staging information.

resultsDuring the study period, 41,924 adults were identified with newly diagnosed cancer, of which 2894 (6.9%) tested positive for SARS-CoV-2. The population consisted of White (n=32,867, 78.4%), Black (n=2671, 6.4%), Hispanic (n=832, 2.0%), and other (n=5554, 13.2%) racial backgrounds, with both male (n=21,354, 50.9%) and female (n=20,570, 49.1%) individuals. In the COVID-19 period analysis, after adjusting for age, sex, race or ethnicity, comorbidities, cancer type, and region, the risk of death increased by 91% (adjusted HR 1.91; 95% CI 1.76-2.09) compared to the pre-COVID-19 period (January 1, 2019, to January 14, 2020) after adjusting for other covariates. In the adjusted time-dependent analysis, SARS-CoV-2 infection was associated with an increase in all-cause mortality (adjusted HR 6.91; 95% CI 6.06-7.89). Mortality increased 2.5 times among adults aged 65 years and older (adjusted HR 2.74; 95% CI 2.26-3.31) compared to adults 18-44 years old, among male (adjusted HR 1.23; 95% CI 1.14-1.32) compared to female individuals, and those with ≥2 chronic conditions (adjusted HR 2.12; 95% CI 1.94-2.31) compared to those with no comorbidities. Risk of mortality was 9% higher in the rural population (adjusted HR 1.09; 95% CI 1.01-1.18) compared to adult urban residents.

conclusionsThe findings highlight increased risk of death is associated with SARS-CoV-2 infection among patients with a recent diagnosis of cancer. Elevated risk underscores the importance of adhering to social distancing, mask adherence, vaccination, and regular testing among the adult cancer population.

Indexed as

cancercancer experiencecancer populationcoronavirusCOVID-19Cox proportional hazards modeldeathEHRelectronic health recordhazard ratioKaplan-Meiermortalityoncologyoutcomepatient with cancerriskSARS-CoV-2survival

Identifiers

PMID36201388
PMCPMC9555821
OpenAlexW4302307854

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.