The Impact of Digital Inequities on Nasal and Paranasal-Sinus Cancer Disparities in the United States: A Cohort Study
Bibliographic record
Abstract
Background: In the modern era, the use of technology can substantially impact care access. Despite the extent of its influence on several chronic medical conditions related to the heart, lungs, and others, the relationship between one's access to digital resources and oncologic conditions has been seldom investigated in select pathologies among gastrointestinal and head-neck regions. However, studies on the influence of this "digital inequity" on other cancers pertaining to nasal and paranasal sinus cancer (NPSC) have yet to be performed. This remains in stark contrast to the extent of large data approaches assessing the impact of traditional social determinants/drivers of health (SDoH), such as factors related to one's socioeconomic status, minoritized race or ethnicity, and housing-transportation status, on prognostic and treatment outcomes. Objective: This study aims to use the Digital Inequity Index (DII), a novel, comprehensive tool that quantifies digital resource access on an area- or community-based level, to assess the relationship between inequities in digital accessibility with NPSC disparities in prognosis and care in the United States. Methods: Patients with NPSC from 2008 to 2017 in the Surveillance, Epidemiology, and End Results Program were assessed for significant regression trends in the long-term follow-up period and treatment receipt across NPSCs with increasing overall digital inequity, as measured by DII. DII was based on 17 census-tract level variables derived from the summarized values overlapping that same time period from the US Census/American Community Survey and Federal Communications Commission Annual Broadband Report. Variables were categorized as infrastructure-access (ie, electronic device ownership, internet provider availability, and income-broadband subscription ratio) or sociodemographic (education, income, age, and disability), ranked, and then averaged into a composite score to encompass direct and indirect factors related to digital inequity. Results: Across 8012 adult patients with NPSC, males (n=5416, 67.6%) and White race (n=4293, 53.6%) were the most represented demographics. With increasing digital inequity, as measured by increasing total DII scores, significant decreases in the length of long-term follow-up were observed with nasopharyngeal (P<.01) and maxillary sinus cancers (P=.02), with decreases as high as 19% (35.2 to 28.5 months, nasopharynx). Electronic device and service availability inequities showcased higher-magnitude contributions to observed associated regression trends, while the income-broadband ratio contributed less. Significantly decreased odds of receiving indicated surgery (lowest odds ratio 0.87, 95% CI 0.80-0.95, maxillary) and radiation (lowest odds ratio 0.78, 95% CI 0.63-0.95, ethmoid) for several NPSCs were also observed. Conclusions: Digital inequities are associated with detrimental NPSC care and surveillance trends in the United States, even when accounting for traditional SDoH factors. These results prompt the need to include digital factors into the discussion of contextualizing SDoH-based analyses of cancer care disparities, as well as the specific factors from which prospective implementations and initiatives can invest limited public health resources to alleviate the most pertinent drivers of disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".