Evidence map›Paper›PMID 38601551›Full record

ArticleHeliyon2024

The effect of COVID-19 on cancer incidences in the U.S.

Ramalingam Shanmugam, Larry Fulton, C Scott Kruse, Brad Beauvais, Jose Betancourt, Gerardo Pacheco, Rohit Pradhan, Keya Sen, Zo Ramamonjiarivelo, Arvind Sharma

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

10 authors at 2 institutions in 1 country.

Ramalingam ShanmugamTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Larry FultonBoston College, Woods College of Advancing Studies, St. Mary's Hall South, Chestnut Hill, MA, 02467, USA.
C Scott KruseTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Brad BeauvaisTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Jose BetancourtTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Gerardo PachecoTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Rohit PradhanTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Keya SenTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Zo RamamonjiariveloTexas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA.
Arvind SharmaBoston College, Woods College of Advancing Studies, St. Mary's Hall South, Chestnut Hill, MA, 02467, USA.
Texas State University · USBoston College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin's lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.

Indexed as

And post COVID-19 indicesAnglesDuringIndicesMost likely cancer incidencePrePrincipal components analysisRadar plot

Identifiers

PMID38601551
PMCPMC11004761
OpenAlexW4393346641

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.