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Record W4402842129 · doi:10.2196/53229

Examining Racial Disparities in Colorectal Cancer Screening and the Role of Online Medical Record Use: Findings From a Cross-Sectional Study of a National Survey

2024· article· en· W4402842129 on OpenAlexvenueno aff
Aldenise Ewing, Daniel Marshall, A. Henry, Mahmoud Abdel‐Rasoul, Skylar McElwain, Jennifer L. Hefner, Portia Zaire, Timiya S. Nolan, Will L. Tarver, Chyke A. Doubeni

Bibliographic record

VenueJMIR Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Cancer Institute
KeywordsPreprintCross-sectional studyColorectal cancerMedicineHealth Information National Trends SurveyGerontologyPsychologyCancerInternal medicinePolitical scienceWorld Wide WebHealth informationComputer scienceHealth carePathology

Abstract

fetched live from OpenAlex

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. Early detection via routine CRC screening can significantly lower risks for CRC-specific morbidity and mortality. Public health initiatives between 2000 and 2015 nearly doubled CRC screening rates for some US adults. However, screening rates remain lowest for adults aged 45-49 years (20%), patients of safety net health care facilities (42%), adults without insurance (44%), and other subgroups compared with national averages (72%). Given the evolving landscape of digital health care and trends in web-based health information-seeking behaviors, leveraging online medical record (OMR) systems may be an underutilized resource to promote CRC screening utilization. Recognizing trends in OMR usage and patient demographics may enhance digital inclusion-a key social determinant of health-and support equitable web-based interventions aimed at boosting CRC screening across diverse populations. Objective: This study examined the association of accessing an OMR with CRC screening utilization and corresponding sociodemographic characteristics of US adults. Methods: In 2023, we conducted a secondary data analysis using a pooled, weighted sample from Health Information National Trends Survey (HINTS) 5 cycles, 2, 3, and 4 (2018-2020), a nationally representative survey assessing how US adults access and use health-related information. We analyzed the association between sociodemographic characteristics, medical conditions, OMR access, and CRC screening behaviors via logistic regression. Results: The sample included adults aged 45-75 years (N=5143). The mean age was 59 (SD 8) years for those who reported CRC screening and 52 (SD 6) years for those never screened. Nearly 70% (4029/5143) of participants reported CRC screening and 52% (2707/5143) reported OMR access in the past year. Adjusted odds of CRC screening were higher among non-Hispanic African American or Black adults than among non-Hispanic White adults (odds ratio [OR] 1.76, 95% CI 1.22-2.53), adults who accessed an OMR (OR 1.89, 95% CI 1.45-2.46), older individuals (OR 1.18, 95% CI 1.16-1.21), the insured (OR 3.69, 95% CI 2.34-5.82), and those with a professional or graduate degree versus those with a high school diploma or less (OR 2.65, 95% CI 1.28-5.47). Individuals aged 65-75 years were significantly more likely (P<.001) to be screened (1687/1831, 91%) than those aged 45-49 years (190/610, 29%). Conclusions: Promoting OMR access, especially among the most disadvantaged Americans, may assist in reaching national screening goals. Emphasis should be placed on the mutability of OMR use compared with most other statistically significant associations with CRC screening behaviors. OMR access provides an intervenable means of promoting CRC education and screening, especially among those facing structural barriers to cancer diagnoses and care. Future research should focus on tailored and accessible interventions that expand OMR access, particularly for younger populations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.374
Teacher spread0.311 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2024
Admission routes1
Has abstractyes

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