Evidence map›Paper›PMID 37366344›Full record

ArticleeLife2023

The impact of COVID-19 on cancer screening and treatment in older adults: The Multiethnic Cohort Study.

Victoria P Mak, Kami White, Lynne R Wilkens, Iona Cheng, Christopher A Haiman, Loic Le Marchand

Open access · goldAbstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 23% 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

7 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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.

Victoria P MakPopulation Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, United States.ORCID 0000-0002-0908-5018
Kami WhitePopulation Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, United States.
Lynne R WilkensPopulation Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, United States.
Iona ChengDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States.
Christopher A HaimanCenter for Genetic Epidemiology, University of Southern California, Los Angeles, United States.ORCID 0000-0002-0097-9971
Loic Le MarchandPopulation Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, United States.ORCID 0000-0001-5013-980X
University of Hawaiʻi at Mānoa · USUniversity of California, San Francisco · USUniversity of Southern California · US

Funding

University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI GERTRAUD MASKARINEC · 1996 to 2026
$56.2M
Understanding Population Differences in Cancer: The MEC StudyU01CA164973 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI HAIMAN, CHRISTOPHER ALAN, LE MARCHAND, LOIC · 2015 to 2025
$37.4M
NCI NIH HHS P30 CA071789NCI NIH HHS U01 CA164973
6 · The paper itself

Abstract

Background: The Coronavirus Disease of 2019 (COVID-19) has impacted the health and day-to-day life of individuals, especially the elderly and people with certain pre-existing medical conditions, including cancer. The purpose of this study was to investigate how COVID-19 impacted access to cancer screenings and treatment, by studying the participants in the Multiethnic Cohort (MEC) study. Methods: The MEC has been following over 215,000 residents of Hawai'i and Los Angeles for the development of cancer and other chronic diseases since 1993-1996. It includes men and women of five racial and ethnic groups: African American, Japanese American, Latino, Native Hawaiian, and White. In 2020, surviving participants were sent an invitation to complete an online survey on the impact of COVID-19 on their daily life activities, including adherence to cancer screening and treatment. Approximately 7,000 MEC participants responded. A cross-sectional analysis was performed to investigate the relationships between the postponement of regular health care visits and cancer screening procedures or treatment with race and ethnicity, age, education, and comorbidity. Results: Women with more education, women with lung disease, COPD, or asthma, and women and men diagnosed with cancer in the past 5 years were more likely to postpone any cancer screening test/procedure due to the COVID-19 pandemic. Groups less likely to postpone cancer screening included older women compared to younger women and Japanese American men and women compared to White men and women. Conclusions: This study revealed specific associations of race/ethnicity, age, education level, and comorbidities with the cancer-related screening and healthcare of MEC participants during the COVID-19 pandemic. Increased monitoring of patients in high-risk groups for cancer and other diseases is of the utmost importance as the chance of undiagnosed cases or poor prognosis is increased as a result of delayed screening and treatment. Funding: This research was partially supported by the Omidyar 'Ohana Foundation and grant U01 CA164973 from the National Cancer Institute.

Indexed as

COVID-19NeoplasmsAgedCohort StudiesCross-Sectional StudiesEarly Detection of CancerFemaleHumansMalePandemicscancer screeningCOVID-19epidemiologyglobal healthhealth-related behaviorslifestyle factorsmultiethnic cohortnone

Identifiers

PMID37366344
PMCPMC10642961
OpenAlexW4382182183

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.